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Awhad, Rahul, Saurabh Jayswal, Adesh More, and Jyoti Kundale. "Fraudulent Face Image Detection." ITM Web of Conferences 32 (2020): 03005. http://dx.doi.org/10.1051/itmconf/20203203005.
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Due to the growing advancements in technology, many software applications are being developed to modify and edit images. Such software can be used to alter images. Nowadays, an altered image is so realistic that it becomes too difficult for a person to identify whether the image is fake or real. Such software applications can be used to alter the image of a person’s face also. So, it becomes very difficult to identify whether the image of the face is real or not. Our proposed system is used to identify whether the image of a face is fake or real. The proposed system makes use of machine learning. The system makes use of a convolution neural network and support vector classifier. Both these machine learning models are trained using real as well as fake images. Both these trained models will take an image as an input and will determine whether the image is fake or real.
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Patil, Priyadarshini, Vipul Deshpande, Vishal Malge, and Abhishek Bevinmanchi. "Fake Face Detection Using CNN." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (September 30, 2022): 519–22. http://dx.doi.org/10.22214/ijraset.2022.45829.
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Abstract: Real and Fake face recognition using CNN and deep learning is presented in the paper. Searching for the authenticity of an image with the naked eye becomes a complicated task in detecting image forgeries. The goal of this study is to evaluate how well different deep learning approaches perform. The initial stage of the proposed strategy is to train several pre-trained deep learning models on the image dataset for recognizing real and fake images to identify fake faces. In order to assess the effectiveness of these models, we consider how well they separate two classes - false and true. Regarding the models tested so far, the VGG models have the best training accuracy (86%) on VGG-16, while VGG-16 shows an excellent test set. accuracy with 10 epochs or less, which is competitively better than all other methods. The outputs of these models were examined to find out exactly
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Shalini, S. "Fake Image Detection." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 15, 2021): 1140–45. http://dx.doi.org/10.22214/ijraset.2021.35238.
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In this technological generation, social media plays an important role in people’s daily life. Most of them share text, images and videos on social media(Instagram, Facebook, Twitter ,etc.,). Images are one of the common types of media share among users on social media. So, there is a chance for monitoring of images contained in social media. So most of the people can fabricate these images and disseminate them widely in a very short time, which treats the creditability of the news and public confidence in the means of social communication. So here this research has attempted to propose an approach which will extract image content, classify it and verify that the image is false or true and uncovers the manipulation. There are many unwanted contents in social media such as threats and forged images, which may cause many issues to the society and also national security. This approach aims to build a model that can be used to classify social media content to detect any threats and forged images.
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ST, Suganthi, Mohamed Uvaze Ahamed Ayoobkhan, Krishna Kumar V, Nebojsa Bacanin, Venkatachalam K, Hubálovský Štěpán, and Trojovský Pavel. "Deep learning model for deep fake face recognition and detection." PeerJ Computer Science 8 (February 22, 2022): e881. http://dx.doi.org/10.7717/peerj-cs.881.
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Deep Learning is an effective technique and used in various fields of natural language processing, computer vision, image processing and machine vision. Deep fakes uses deep learning technique to synthesis and manipulate image of a person in which human beings cannot distinguish the fake one. By using generative adversarial neural networks (GAN) deep fakes are generated which may threaten the public. Detecting deep fake image content plays a vital role. Many research works have been done in detection of deep fakes in image manipulation. The main issues in the existing techniques are inaccurate, consumption time is high. In this work we implement detecting of deep fake face image analysis using deep learning technique of fisherface using Local Binary Pattern Histogram (FF-LBPH). Fisherface algorithm is used to recognize the face by reduction of the dimension in the face space using LBPH. Then apply DBN with RBM for deep fake detection classifier. The public data sets used in this work are FFHQ, 100K-Faces DFFD, CASIA-WebFace.
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Ruffin, Margie, Gang Wang, and Kirill Levchenko. "Explaining Why Fake Photos are Fake." Proceedings of the ACM on Human-Computer Interaction 7, GROUP (December 29, 2022): 1–22. http://dx.doi.org/10.1145/3567558.
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Today's disinformation campaigns may use deceptively altered photographs to promote a false narrative. In some cases, viewers may be unaware of the alteration and thus may more readily accept the promoted narrative. In this work, we consider whether this effect can be lessened by explaining to the viewer how an image has been manipulated. To explore this idea, we conduct a two-part study. We started with a survey (n=113) to examine whether users are indeed bad at identifying manipulated images. Our result validated this conjecture as participants performed barely better than random guessing (60% accuracy). Then we explored our main hypothesis in a second survey (n=543). We selected manipulated images circulated on the Internet that pictured political figures and opinion influencers. Participants were divided into three groups to view the original (unaltered) images, the manipulated images, and the manipulated images with explanations, respectively. Each image represents a single case study and is evaluated independently of the others. We find that simply highlighting and explaining the manipulation to users was not always effective. When it was effective, it did help to make users less agreeing with the intended messages behind the manipulation. However, surprisingly, the explanation also had an opposite (e.g.,negative) effect on users' feeling/sentiment toward the subjects in the images. Based on these results, we discuss open-ended questions which could serve as the basis for future research in this area.
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Hsu, Chih-Chung, Yi-Xiu Zhuang, and Chia-Yen Lee. "Deep Fake Image Detection Based on Pairwise Learning." Applied Sciences 10, no. 1 (January 3, 2020): 370. http://dx.doi.org/10.3390/app10010370.
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Generative adversarial networks (GANs) can be used to generate a photo-realistic image from a low-dimension random noise. Such a synthesized (fake) image with inappropriate content can be used on social media networks, which can cause severe problems. With the aim to successfully detect fake images, an effective and efficient image forgery detector is necessary. However, conventional image forgery detectors fail to recognize fake images generated by the GAN-based generator since these images are generated and manipulated from the source image. Therefore, in this paper, we propose a deep learning-based approach for detecting the fake images by using the contrastive loss. First, several state-of-the-art GANs are employed to generate the fake–real image pairs. Next, the reduced DenseNet is developed to a two-streamed network structure to allow pairwise information as the input. Then, the proposed common fake feature network is trained using the pairwise learning to distinguish the features between the fake and real images. Finally, a classification layer is concatenated to the proposed common fake feature network to detect whether the input image is fake or real. The experimental results demonstrated that the proposed method significantly outperformed other state-of-the-art fake image detectors.
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Tanaka, Miki, Sayaka Shiota, and Hitoshi Kiya. "A Detection Method of Operated Fake-Images Using Robust Hashing." Journal of Imaging 7, no. 8 (August 5, 2021): 134. http://dx.doi.org/10.3390/jimaging7080134.
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SNS providers are known to carry out the recompression and resizing of uploaded images, but most conventional methods for detecting fake images/tampered images are not robust enough against such operations. In this paper, we propose a novel method for detecting fake images, including distortion caused by image operations such as image compression and resizing. We select a robust hashing method, which retrieves images similar to a query image, for fake-image/tampered-image detection, and hash values extracted from both reference and query images are used to robustly detect fake-images for the first time. If there is an original hash code from a reference image for comparison, the proposed method can more robustly detect fake images than conventional methods. One of the practical applications of this method is to monitor images, including synthetic ones sold by a company. In experiments, the proposed fake-image detection is demonstrated to outperform state-of-the-art methods under the use of various datasets including fake images generated with GANs.
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Tang, Guihua, Lei Sun, Xiuqing Mao, Song Guo, Hongmeng Zhang, and Xiaoqin Wang. "Detection of GAN-Synthesized Image Based on Discrete Wavelet Transform." Security and Communication Networks 2021 (June 15, 2021): 1–10. http://dx.doi.org/10.1155/2021/5511435.
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Recently, generative adversarial networks (GANs) and its variants have shown impressive ability in image synthesis. The synthesized fake images spread widely on the Internet, and it is challenging for Internet users to identify the authenticity, which poses huge security risk to the society. However, compared with the powerful image synthesis technology, the detection of GAN-synthesized images is still in its infancy and face a variety of challenges. In this study, a method named fake images discriminator (FID) is proposed, which detects that GAN-synthesized fake images use the strong spectral correlation in the imaging process of natural color images. The proposed method first converts the color image into three color components of R, G, and B. Discrete wavelet transform (DWT) is then applied to RGB components separately. Finally, the correlation coefficient between the subband images is used as a feature vector for authenticity classification. Experimental results show that the proposed FID method achieves impressive effectiveness on the StyleGAN2-synthesized faces and multitype fake images synthesized with the state-of-the-art GANs. Also, the FID method exhibits good robustness against the four common perturbation attacks.
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Lorenz, Esther. "Real Image, Fake Estate." International Journal of Design in Society 6, no. 2 (2013): 11–26. http://dx.doi.org/10.18848/2325-1328/cgp/v06i02/38500.
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Guo, Yuanfang, Xiaochun Cao, Wei Zhang, and Rui Wang. "Fake Colorized Image Detection." IEEE Transactions on Information Forensics and Security 13, no. 8 (August 2018): 1932–44. http://dx.doi.org/10.1109/tifs.2018.2806926.
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Thiruvaazhi Uloli, R. M. Koushal Akash, A. G. Keerthika, and K. S. Dhanwanth. "Survey of Fake Image Synthesis and its Detection." Journal of Innovative Image Processing 4, no. 4 (January 21, 2023): 278–97. http://dx.doi.org/10.36548/jiip.2022.4.006.
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In recent times, image synthesis has attracted significant attention of people for both positive and negative reasons. Images can be easily synthesized using various techniques. This paper surveys various techniques for image synthesis as well as its detection in a unique structured manner, to enable a perspective on this iterative phenomenon. The paper describes both advantages and limitations starting from simple fake image detection to AI synthesized image detection approaches that are available in the literature. Generative Adversarial Network (GAN) is the trending algorithm for artificial image synthesis, because the faces generated by GAN are highly realistic. As discriminators are already present in the GAN’s structure, any attempt to create a distinguisher that detects fake images synthesized by GAN, needs to structure itself to detect all existing patterns of fake image synthesis including that of GAN.
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Zhang, Weiguo, and Chenggang Zhao. "Exposing Face-Swap Images Based on Deep Learning and ELA Detection." Proceedings 46, no. 1 (November 17, 2019): 29. http://dx.doi.org/10.3390/ecea-5-06684.
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New developments in artificial intelligence (AI) have significantly improved the quality and efficiency in generating fake face images; for example, the face manipulations by DeepFake are so realistic that it is difficult to distinguish their authenticity—either automatically or by humans. In order to enhance the efficiency of distinguishing facial images generated by AI from real facial images, a novel model has been developed based on deep learning and error level analysis (ELA) detection, which is related to entropy and information theory, such as cross-entropy loss function in the final Softmax layer, normalized mutual information in image preprocessing, and some applications of an encoder based on information theory. Due to the limitations of computing resources and production time, the DeepFake algorithm can only generate limited resolutions, resulting in two different image compression ratios between the fake face area as the foreground and the original area as the background, which leaves distinctive artifacts. By using the error level analysis detection method, we can detect the presence or absence of different image compression ratios and then use Convolution neural network (CNN) to detect whether the image is fake. Experiments show that the training efficiency of the CNN model can be significantly improved by using the ELA method. And the detection accuracy rate can reach more than 97% based on CNN architecture of this method. Compared to the state-of-the-art models, the proposed model has the advantages such as fewer layers, shorter training time, and higher efficiency.
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C, Ms Faseela Kathun. "Generating faces From the Sketch Using GAN." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (October 31, 2021): 1–3. http://dx.doi.org/10.22214/ijraset.2021.38259.
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Abstract: In most cases, sketch images simply show basic profile details and do not include facial detail. As a result, precisely generating facial features is difficult. Using the created adversarial network and attributes, we propose an image translation network. A feature extracting network and a down-sampling up-sampling network make up the generator network. There is a generator and a discriminator in GANs. The Generator creates fake data samples (images, audio, etc.) in intended to mislead the Discriminator. On the other hand, the Discriminator attempts to distinguish between the real and fake sample Keywords: Deep Learning, Generative Adversarial Networks, Image Translation, face generation, skip-connection.
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C, Ms Faseela Kathun. "Generating faces From the Sketch Using GAN." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (October 31, 2021): 1–3. http://dx.doi.org/10.22214/ijraset.2021.38259.
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Abstract: In most cases, sketch images simply show basic profile details and do not include facial detail. As a result, precisely generating facial features is difficult. Using the created adversarial network and attributes, we propose an image translation network. A feature extracting network and a down-sampling up-sampling network make up the generator network. There is a generator and a discriminator in GANs. The Generator creates fake data samples (images, audio, etc.) in intended to mislead the Discriminator. On the other hand, the Discriminator attempts to distinguish between the real and fake sample Keywords: Deep Learning, Generative Adversarial Networks, Image Translation, face generation, skip-connection.
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Gayadhankar, Kaustubh, Rishi Patel, Hrithik Lodha, and Swapnil Shinde. "Image plagiarism detection using GAN - (Generative Adversarial Network)." ITM Web of Conferences 40 (2021): 03013. http://dx.doi.org/10.1051/itmconf/20214003013.
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In Today’s date plagiarism is a very important aspect because content originality is the client's prior requirement. Many people on the internet use others' images and get publicity while the owner of the image or data won′t get anything out of it. Many users copy the data or image features from the other users and modify it a little bit or create an artificial replica of it. With sufficient computational power and volume of data, the GAN models are capable enough to produce fake images that look very much similar to the real images. These kinds of images are generally not detected by modern plagiarism systems. GAN stands for generative adversarial network. It has two neural networks working inside. The first one is the generator which generates a random image and the second one is the discriminator which identifies whether the image being generated is a real or a fake image. In this paper, we have proposed a system that has been trained on both fake images (GAN Generated images) and real images and will help us in flagging whether the image is plagiarised or a real image.
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Segura-Bedmar, Isabel, and Santiago Alonso-Bartolome. "Multimodal Fake News Detection." Information 13, no. 6 (June 2, 2022): 284. http://dx.doi.org/10.3390/info13060284.
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Over the last few years, there has been an unprecedented proliferation of fake news. As a consequence, we are more susceptible to the pernicious impact that misinformation and disinformation spreading can have on different segments of our society. Thus, the development of tools for the automatic detection of fake news plays an important role in the prevention of its negative effects. Most attempts to detect and classify false content focus only on using textual information. Multimodal approaches are less frequent and they typically classify news either as true or fake. In this work, we perform a fine-grained classification of fake news on the Fakeddit dataset, using both unimodal and multimodal approaches. Our experiments show that the multimodal approach based on a Convolutional Neural Network (CNN) architecture combining text and image data achieves the best results, with an accuracy of 87%. Some fake news categories, such as Manipulated content, Satire, or False connection, strongly benefit from the use of images. Using images also improves the results of the other categories but with less impact. Regarding the unimodal approaches using only text, Bidirectional Encoder Representations from Transformers (BERT) is the best model, with an accuracy of 78%. Exploiting both text and image data significantly improves the performance of fake news detection.
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Parashar, Amrita, Dr Arvind Kumar Upadhyay, and Dr Kamlesh Gupta. "A Novel Machine Learning Approach for Forgery Detection and Verification in Digital Image." ECS Transactions 107, no. 1 (April 24, 2022): 11791–98. http://dx.doi.org/10.1149/10701.11791ecst.
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Image manipulation can cause many ethical, economical, and political issues for everyone. It can be used to create fake information, fake ids, fake online profiles, fake news. A proper technique for image detection and verification is thus, a hot research area, which helps the affected people to overcome the forgery attacks. Due to the increased interaction among people through social networking sites, image forgery is prevalent and it is an essential activity to detect any forged image. This paper’s main objective is to identify fraud or tampered images effectively. By using the methods of copy move forgery detection, the obtained results are more accurate in a minimum time as compared to previous algorithms.
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Liu, Haiqing, Shiqiang Zheng, Shuhua Hao, and Yuancheng Li. "Multifeature Fusion Detection Method for Fake Face Attack in Identity Authentication." Advances in Multimedia 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/9025458.
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With the rise in biometric-based identity authentication, facial recognition software has already stimulated interesting research. However, facial recognition has also been subjected to criticism due to security concerns. The main attack methods include photo, video, and three-dimensional model attacks. In this paper, we propose a multifeature fusion scheme that combines dynamic and static joint analysis to detect fake face attacks. Since the texture differences between the real and the fake faces can be easily detected, LBP (local binary patter) texture operators and optical flow algorithms are often merged. Basic LBP methods are also modified by considering the nearest neighbour binary computing method instead of the fixed centre pixel method; the traditional optical flow algorithm is also modified by applying the multifusion feature superposition method, which reduces the noise of the image. In the pyramid model, image processing is performed in each layer by using block calculations that form multiple block images. The features of the image are obtained via two fused algorithms (MOLF), which are then trained and tested separately by an SVM classifier. Experimental results show that this method can improve detection accuracy while also reducing computational complexity. In this paper, we use the CASIA, PRINT-ATTACK, and REPLAY-ATTACK database to compare the various LBP algorithms that incorporate optical flow and fusion algorithms.
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Costa de Beauregard, Raphaëlle. "Fake Paintings, Fake Clues, and True Crimes in French Cinema (1911-1914)." Kronoscope 15, no. 2 (September 1, 2015): 230–45. http://dx.doi.org/10.1163/15685241-12341337.
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A major consequence of the capture of images by photography was a reevaluation of idealist philosophy on behalf of material philosophy. But with the projection of images in movement, the capture of a much more immaterial essence of this reality was foregrounded. Two French films of the period when cinema had fully developed its narrative strategies are examined in this paper: a short burlesque, Jean Durand’sLe Rembrandt de la rue Lepic(Gaumont, 1911), and a serial of five films, Louis Feuillade’sFantômas(Gaumont, 1913-1914). The two films rely on the instability of images for the dramatic progression of plots that are devoted to the pursuit of an “authentic” image, whether a painting or a fingerprint, itself understood as the reliable “trace” of the materiality of the real world. This paper examines the various meanings of the word “trace” in these films within the cultural context of the 1910s and the prominent questioning of time issues.
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Swathi, K., and K. P. Mohanan. "Image Based Fake Indian Coin Detection." International Journal of Computer Sciences and Engineering 06, no. 06 (July 31, 2018): 107–9. http://dx.doi.org/10.26438/ijcse/v6si6.107109.
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CHARAN, RAM, N. KAVYA, and P. BHAVANA. "FAKE CURRENCY DETECTION USING IMAGE PROCESSING." International Journal of Computer Science and Mobile Computing 11, no. 1 (January 30, 2022): 101–7. http://dx.doi.org/10.47760/ijcsmc.2022.v11i01.012.
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The advent of color printing technology has also allowed for counterfeit currency notes to be printed and duplicated on a large scale. This has been beneficial and used by those who want to work on high notes. Due to the existence of counterfeit documents, India is plagued by various problems such as black money and corruption. This has led to a sharp decline in the value of the country's currency. The following article describes an image processing system that can verify the authenticity of notes in Indian rupee.
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Agasti, Tushar, Gajanan Burand, Pratik Wade, and P. Chitra. "Fake currency detection using image processing." IOP Conference Series: Materials Science and Engineering 263 (November 2017): 052047. http://dx.doi.org/10.1088/1757-899x/263/5/052047.
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Sun, Wei, and Wei Lu. "Watermark-based digital fake image detection." International Journal of Computer Applications in Technology 38, no. 1/2/3 (2010): 113. http://dx.doi.org/10.1504/ijcat.2010.034146.
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Pang, Lu Lu, Cong Li Li, De Ning Qi, and Tao Zou. "A New Image Quality Assessment Method Based on SSIM and TV Model." Applied Mechanics and Materials 65 (June 2011): 542–50. http://dx.doi.org/10.4028/www.scientific.net/amm.65.542.
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In this paper, a new image quality assessment method has been proposed in which can judge the quality of images without explicit knowledge of the reference images ,it is based on the SSIM(Structural Similarity) and TV(total variation) model. Firstly, add noises to distorted image to quantitatively determine, it can get the degraded image; secondly, use the improved self-adaptive gradient weights of the TV algorithms to denoising the distorted image, it can get the “fake” reference image, then use the classical SSIM methods to make reference evaluation between the distorted image and the “fake” reference image, after modified, the results is the no reference evaluating indicator. The experiment separated use the standard testing images and the degraded images from the LIVE database to make evaluate experiment, the result show that it is consistent to the result of MOS. This method is no need of reference images, it can use widely.
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Gupta, Agrima, and Rashleen Kour. "Fake Currency Detection Using ORB Algorithm." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (November 30, 2022): 1813–17. http://dx.doi.org/10.22214/ijraset.2022.47726.
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Abstract: This paper presents the technique to detect fake currency using the technique of image processing. Fake currency is produced and circulated in the market by antisocial people or group of people in the society. Fake currency affects the economic status of the country and also brings disturbance in the economic development. Various methods of image processing techniques that can be used to process and detect fake currency are- image segmentation, image acquisition, edge detection, grey scale conversion, feature extraction and feature matching. The system presented in the paper deals with feature extraction and feature matching method of image processing in python language. Result will be displayed in terms of accuracy value depicting whether the currency note is original or fake.
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Shahar, Hadas, and Hagit Hel-Or. "Fake Video Detection Using Facial Color." Color and Imaging Conference 2020, no. 28 (November 4, 2020): 175–80. http://dx.doi.org/10.2352/issn.2169-2629.2020.28.27.
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The field of image forgery is widely studied, and with the recent introduction of deep networks based image synthesis, detection of fake image sequences has increased the challenge. Specifically, detecting spoofing attacks is of grave importance. In this study we exploit the minute changes in facial color of human faces in videos to determine real from fake videos. Even when idle, human skin color changes with sub-dermal blood flow, these changes are enhanced under stress and emotion. We show that extracting facial color along a video sequence can serve as a feature for training deep neural networks to successfully determine fake vs real face sequences.
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Florea, Ștefan. "MODERN MAN BETWEEN THE IMAGE OF GLORY AND FAKE IMAGES." Altarul Reîntregirii, Suplim.2 (2017): 295–310. http://dx.doi.org/10.29302/ar.2017.suplim.2.21.
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Chen, Xianglong, Haipeng Wang, Yaohui Liang, Ying Meng, and Shifeng Wang. "A Novel Infrared and Visible Image Fusion Approach Based on Adversarial Neural Network." Sensors 22, no. 1 (December 31, 2021): 304. http://dx.doi.org/10.3390/s22010304.
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The presence of fake pictures affects the reliability of visible face images under specific circumstances. This paper presents a novel adversarial neural network designed named as the FTSGAN for infrared and visible image fusion and we utilize FTSGAN model to fuse the face image features of infrared and visible image to improve the effect of face recognition. In FTSGAN model design, the Frobenius norm (F), total variation norm (TV), and structural similarity index measure (SSIM) are employed. The F and TV are used to limit the gray level and the gradient of the image, while the SSIM is used to limit the image structure. The FTSGAN fuses infrared and visible face images that contains bio-information for heterogeneous face recognition tasks. Experiments based on the FTSGAN using hundreds of face images demonstrate its excellent performance. The principal component analysis (PCA) and linear discrimination analysis (LDA) are involved in face recognition. The face recognition performance after fusion improved by 1.9% compared to that before fusion, and the final face recognition rate was 94.4%. This proposed method has better quality, faster rate, and is more robust than the methods that only use visible images for face recognition.
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Raza, Ali, Kashif Munir, and Mubarak Almutairi. "A Novel Deep Learning Approach for Deepfake Image Detection." Applied Sciences 12, no. 19 (September 29, 2022): 9820. http://dx.doi.org/10.3390/app12199820.
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Deepfake is utilized in synthetic media to generate fake visual and audio content based on a person’s existing media. The deepfake replaces a person’s face and voice with fake media to make it realistic-looking. Fake media content generation is unethical and a threat to the community. Nowadays, deepfakes are highly misused in cybercrimes for identity theft, cyber extortion, fake news, financial fraud, celebrity fake obscenity videos for blackmailing, and many more. According to a recent Sensity report, over 96% of the deepfakes are of obscene content, with most victims being from the United Kingdom, United States, Canada, India, and South Korea. In 2019, cybercriminals generated fake audio content of a chief executive officer to call his organization and ask them to transfer $243,000 to their bank account. Deepfake crimes are rising daily. Deepfake media detection is a big challenge and has high demand in digital forensics. An advanced research approach must be built to protect the victims from blackmailing by detecting deepfake content. The primary aim of our research study is to detect deepfake media using an efficient framework. A novel deepfake predictor (DFP) approach based on a hybrid of VGG16 and convolutional neural network architecture is proposed in this study. The deepfake dataset based on real and fake faces is utilized for building neural network techniques. The Xception, NAS-Net, Mobile Net, and VGG16 are the transfer learning techniques employed in comparison. The proposed DFP approach achieved 95% precision and 94% accuracy for deepfake detection. Our novel proposed DFP approach outperformed transfer learning techniques and other state-of-the-art studies. Our novel research approach helps cybersecurity professionals overcome deepfake-related cybercrimes by accurately detecting the deepfake content and saving the deepfake victims from blackmailing.
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Bayoumi, Razan, Marco Alfonse, Mohamed Roushdy, and Abdel-Badeeh M. Salem. "Text-to-image generation based on AttnDM-GAN and DMAttn-GAN: applications and challenges." Bulletin of Electrical Engineering and Informatics 12, no. 2 (April 1, 2023): 1180–88. http://dx.doi.org/10.11591/eei.v12i2.4199.
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The deep fake faces generation using generative adversarial networks (GANs) has reached an incredible level of realism where people can’t differentiate the real from the fake. Text-to-face is a very challenging task compared to other text-to-image syntheses because of the detailed, precise, and complex nature of the human faces in addition to the textual description details. Providing an accurate realistic text-to-image model can be useful for many applications such as criminal identification where the model will be acting as the forensic artist. This paper presents text-to-image generation based on attention dynamic memory (AttnDM-GAN) and dynamic memory attention (DMAttn-GAN) that are applied to different datasets with an analysis that shows the different complexity of different datasets’ categories, the quality of the datasets, and their effect on the results of the resolution and consistency of the generated images.
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Faroek, Dewi, Rusydi Umar, and Imam Riadi. "Classification Based on Machine Learning Methods for Identification of Image Matching Achievements." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 6, no. 2 (April 20, 2022): 198–206. http://dx.doi.org/10.29207/resti.v6i2.3826.
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Classification is one method in image processing. Image processing to search for similar images or with similarity ownership is called image matching or image matching. In the measurement of image matching, the original and fake logo objects are used. Identification of similarity manually with the help of human vision is not necessarily precise and it is difficult to obtain accurate results. Based on this, the identification of image matching of the original and fake logos automatically requires an application, in order to obtain precise and more accurate results. Identification of image suitability is determined through the image segmentation process, and feature extraction is based on the statistics of Red-Green-Blue (RGB), Hue-Saturation-Value (HSV), feature extraction of area, perimeter, eccentricity, and tangent distance measurements. The purpose of this study includes the identification of the achievement of image-matching logo images with comparisons of accuracy between various machine learning methods. The use of machine learning methods in this study includes the k-Nearest Neighbor (kNN), Random Forest (RF), and Multilayer Perceptron (MLP) methods. The use of the dataset includes eighteen training data and eight logo image testing data, divided into genuine and fake classes. The results of the measurement of the accuracy value obtained a value of seventy-five percent with the kNN method or the RF method, while the MLP method obtained an accuracy value of eighty-seven point five percent. Based on these results, it can be concluded that the MLP method with the highest accuracy value was chosen as a classification model from machine learning to identify the achievement of image matching on the original and fake logos. For further development, the system can be developed using other methods or a combination of different methods, in order to obtain better accurate results.
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P, Harshith, Kiran Kumar S R, KorniSesidhar Reddy, Srujan Reddy N, and Jyothi M. "SECRET IMAGE SHARING AND IDENTIFYING FAKE IMAGE USING BLOCK CHAIN." International Research Journal of Computer Science 9, no. 8 (August 13, 2022): 262–66. http://dx.doi.org/10.26562/irjcs.2022.v0908.21.
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The detection of fraudulent images and the attribution of image sources has become a hot topic of debate in both the news industry and social media. Anyone may quickly create or edit digital content in the age of digitalization, and they can then simply broadcast it on social media networks. The use of these social networking sites has created new difficulties for practical application, such as the viral transmission of incorrect or misleading material with harmful intentions. On the one hand, they greatly facilitate modern communication. A currently very emerging technology for data sharing and application is block chain. By using data encryption, timestamps, and distributed consensus, it is possible to exchange decentralised information in distributed systems without relying on one another, which increases the effectiveness of data sharing and application. The massive data remote sensing image system can fully exploit this technology, and the multi-system shared node storage system can be controlled effectively and uniformly to increase the system's economic efficiency. This study suggests important research technologies, builds the shared architecture based on block chain technology, and offers a theoretical framework for non-engineering practise.
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Sung, Thai Leang, and Hyo Jong Lee. "Image-to-Image Translation Using Identical-Pair Adversarial Networks." Applied Sciences 9, no. 13 (June 30, 2019): 2668. http://dx.doi.org/10.3390/app9132668.
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We propose Identical-pair Adversarial Networks (iPANs) to solve image-to-image translation problems, such as aerial-to-map, edge-to-photo, de-raining, and night-to-daytime. Our iPANs rely mainly on the effectiveness of adversarial loss function and its network architectures. Our iPANs consist of two main networks, an image transformation network T and a discriminative network D. We use U-NET for the transformation network T and a perceptual similarity network, which has two streams of VGG16 that share the same weights for network D. Our proposed adversarial losses play a minimax game against each other based on a real identical-pair and a fake identical-pair distinguished by the discriminative network D; e.g. a discriminative network D considers two inputs as a real pair only when they are identical, otherwise a fake pair. Meanwhile, the transformation network T tries to persuade the discriminator network D that the fake pair is a real pair. We experimented on several problems of image-to-image translation and achieved results that are comparable to those of some existing approaches, such as pix2pix, and PAN.
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Zhao, Yufeng, Meng Zhao, Fan Shi, Chen Jia, and Shengyong Chen. "Light-field imaging for distinguishing fake pedestrians using convolutional neural networks." International Journal of Advanced Robotic Systems 18, no. 1 (January 1, 2021): 172988142098740. http://dx.doi.org/10.1177/1729881420987400.
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Pedestrian detection plays an important role in automatic driving system and intelligent robots, and has made great progress in recent years. Identifying the pedestrians from confused planar objects is a challenging problem in the field of pedestrian recognition. In this article, we focus on the 2D fake pedestrian identification based on light-field (LF) imaging and convolutional neural network (CNN). First, we expand the previous dataset to 1500 samples, which is a mid-size dataset for LF images in all public LF datasets. Second, a joint CNN classification framework is proposed, which uses both RGB image and depth image (extracted from the LF image) as input. This framework can fully mine 2D feature information and depth feature information from corresponding images. The experimental results show that the proposed method is efficient to identify the fake pedestrian in a 2D plane and achieves a recognition accuracy of 97.0%. This work is expected to be used in recognition of 2D fake pedestrian and may help researchers solve other computer vision problems.
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Xue, Ziyu, Xiuhua Jiang, Qingtong Liu, and Zhaoshan Wei. "Global–Local Facial Fusion Based GAN Generated Fake Face Detection." Sensors 23, no. 2 (January 5, 2023): 616. http://dx.doi.org/10.3390/s23020616.
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Media content forgery is widely spread over the Internet and has raised severe societal concerns. With the development of deep learning, new technologies such as generative adversarial networks (GANs) and media forgery technology have already been utilized for politicians and celebrity forgery, which has a terrible impact on society. Existing GAN-generated face detection approaches rely on detecting image artifacts and the generated traces. However, these methods are model-specific, and the performance is deteriorated when faced with more complicated methods. What’s more, it is challenging to identify forgery images with perturbations such as JPEG compression, gamma correction, and other disturbances. In this paper, we propose a global–local facial fusion network, namely GLFNet, to fully exploit the local physiological and global receptive features. Specifically, GLFNet consists of two branches, i.e., the local region detection branch and the global detection branch. The former branch detects the forged traces from the facial parts, such as the iris and pupils. The latter branch adopts a residual connection to distinguish real images from fake ones. GLFNet obtains forged traces through various ways by combining physiological characteristics with deep learning. The method is stable with physiological properties when learning the deep learning features. As a result, it is more robust than the single-class detection methods. Experimental results on two benchmarks have demonstrated superiority and generalization compared with other methods.
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Chung, H. Y., and C. W. S. Chan. "POS0990 Deep learning algorithms for magnetic resonance imaging of inflammatory sacroiliitis in axial spondyloarthritis." Annals of the Rheumatic Diseases 81, Suppl 1 (May 23, 2022): 803.1–803. http://dx.doi.org/10.1136/annrheumdis-2022-eular.3420.
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BackgroundMagnetic resonance imaging (MRI) of the sacroiliac (SI) joints is increasingly important in the management of axial spondyloarthritis (SpA). Artificial intelligence (AI) may be the next crucial step in enabling the widespread application of MRI.ObjectivesTo develop a deep learning algorithm for detection of active inflammatory sacroiliitis in short tau inversion recovery (STIR) sequence MRI.MethodsA total of 326 participants with axial spondyloarthritis (SpA), and 63 participants with non-specific back pain (NSBP) were recruited. STIR MRI of the SI joints was performed and clinical data were collected. Region of interests (ROIs) were drawn outlining bone marrow edema, a reliable marker of active inflammation, which formed the ground truth masks from which “fake-colour” images were derived. Both the original and “fake-colour” images were randomly allocated into either the training and validation dataset or the testing dataset. Attention U-net was used for the development of deep learning algorithms. As comparison, an independent radiologist and rheumatologist blinded to the ground truth masks, were tasked with identifying bone marrow edema in the MR images.ResultsInflammatory sacroiliitis were identified in 1398 MR images from 228 participants. No inflammation was found in 3944 MR images from 161 participants. The mean sensitivity of algorithms derived from the original dataset and “fake-colour” image dataset were 0.86±0.02, and 0.90±0.01 respectively. The mean specificity of algorithms derived from the original and “fake-colour” image dataset were 0.92±0.02, and 0.93±0.01 respectively. The mean testing dice coefficients were 0.48± 0.27 for the original dataset and 0.51±0.25 for the “fake-colour” image dataset. The area under the curve of the receiver operating characteristic (AUC-ROC) curve of the algorithms using original dataset and “fake-colour” image dataset were 0.92 and 0.96 respectively. Sensitivity and specificity of algorithms were comparable to interpretation by a radiologist, but outperformed the rheumatologist.ConclusionAn MRI deep learning algorithm was developed for detection of inflammatory sacroiliitis in axial SpA.Disclosure of InterestsNone declared
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Shiby, Ashik. "Detection of Fake Currency using Image Processing." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 10, 2021): 419–23. http://dx.doi.org/10.22214/ijraset.2021.36348.
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In its definition, the term 'currency' defines an agreed-upon exchange item, the national currency being the legal entity used by the selected controlling entity. Throughout history, issuers have faced 1 common threat: counterfeit. In recent years fake money note has been printed that has resulted in significant losses and damage to society. Therefore, it becomes necessary to build a tool for earning money. This research project proposes a way to look at the note of counterfeit money distributed in our country through their image. After selecting an image use pre-processing. In pre-processing, the acquired image is cropped, smooth, and adjust. Change the image to grey-scale. After conversion use image separation. Features are extracted and reduce. Finally, compare the picture to be real or fake. Duplicate money has been a major problem in the market. There are currency counting machines available in banks and other trading venues to check financial authenticity. Most people do not have access to such programs which is why there is a need for fake money laundering software, which can be used by ordinary people. This proposed framework uses Image Processing to determine whether the money is real or counterfeit. The research project program is built entirely using Python's programming language. It has the methods such as grayscale conversion, edge detection, segmentation, etc.
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LU, W. "Blind Fake Image Detection Scheme Using SVD." IEICE Transactions on Communications E89-B, no. 5 (May 1, 2006): 1726–28. http://dx.doi.org/10.1093/ietcom/e89-b.5.1726.
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Liu, Peng, Fuyu Li, Shanshan Yuan, and Wanyi Li. "Unsupervised Image-Generation Enhanced Adaptation for Object Detection in Thermal Images." Mobile Information Systems 2021 (December 27, 2021): 1–6. http://dx.doi.org/10.1155/2021/1837894.
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Object detection in thermal images is an important computer vision task and has many applications such as unmanned vehicles, robotics, surveillance, and night vision. Deep learning-based detectors have achieved major progress, which usually need large amount of labelled training data. However, labelled data for object detection in thermal images is scarce and expensive to collect. How to take advantage of the large number labelled visible images and adapt them into thermal image domain is expected to solve. This paper proposes an unsupervised image-generation enhanced adaptation method for object detection in thermal images. To reduce the gap between visible domain and thermal domain, the proposed method manages to generate simulated fake thermal images that are similar to the target images and preserves the annotation information of the visible source domain. The image generation includes a CycleGAN-based image-to-image translation and an intensity inversion transformation. Generated fake thermal images are used as renewed source domain, and then the off-the-shelf domain adaptive faster RCNN is utilized to reduce the gap between the generated intermediate domain and the thermal target domain. Experiments demonstrate the effectiveness and superiority of the proposed method.
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Wang, Jingzi, Hongyan Mao, and Hongwei Li. "FMFN: Fine-Grained Multimodal Fusion Networks for Fake News Detection." Applied Sciences 12, no. 3 (January 21, 2022): 1093. http://dx.doi.org/10.3390/app12031093.
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As one of the most popular social media platforms, microblogs are ideal places for news propagation. In microblogs, tweets with both text and images are more likely to attract attention than text-only tweets. This advantage is exploited by fake news producers to publish fake news, which has a devasting impact on individuals and society. Thus, multimodal fake news detection has attracted the attention of many researchers. For news with text and image, multimodal fake news detection utilizes both text and image information to determine the authenticity of news. Most of the existing methods for multimodal fake news detection obtain a joint representation by simply concatenating a vector representation of the text and a visual representation of the image, which ignores the dependencies between them. Although there are a small number of approaches that use the attention mechanism to fuse them, they are not fine-grained enough in feature fusion. The reason is that, for a given image, there are multiple visual features and certain correlations between these features. They do not use multiple feature vectors representing different visual features to fuse with textual features, and ignore the correlations, resulting in inadequate fusion of textual features and visual features. In this paper, we propose a novel fine-grained multimodal fusion network (FMFN) to fully fuse textual features and visual features for fake news detection. Scaled dot-product attention is utilized to fuse word embeddings of words in the text and multiple feature vectors representing different features of the image, which not only considers the correlations between different visual features but also better captures the dependencies between textual features and visual features. We conduct extensive experiments on a public Weibo dataset. Our approach achieves competitive results compared with other methods for fusing visual representation and text representation, which demonstrates that the joint representation learned by the FMFN (which fuses multiple visual features and multiple textual features) is better than the joint representation obtained by fusing a visual representation and a text representation in determining fake news.
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Tao, Feng, and Yunyu Dang. "Virtuality, simulation and fake." Prometeica - Revista de Filosofía y Ciencias, Especial (August 11, 2022): 36–51. http://dx.doi.org/10.34024/prometeica.2022.especial.13527.
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As a real-life application of a “virtual human,” virtual anchors refer to the application of virtual reality technology in communication hosting to create virtual images that simulate human anchors. The pursuit of a virtual human image originates from philosophy, art, and biotechnology. Virtual anchors are also required to undertake the function of communication and hosting. First, by sorting out the development process of virtual anchors, we find that the development of a virtual anchor is based on the needs of technological development and the purpose of capital profit. Second, the simulation technology of a virtual anchor has five dimensions, such as appearance, individuality, and autonomy, and two levels: internal and external. This simulation technology has reached the intelligent simulation stage. Finally, the technology involved in virtual anchors will lead to a trust crisis. That is, the people's body-mind relation and cognitive trust will be broken under the mediation of data. Virtual technology will recreate a false aura, that is, false space-time and fake original works, which is the intelligent falsehood required by the new cultural industry.
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Marcialis, Gian Luca, Pietro Coli, and Fabio Roli. "Fingerprint Liveness Detection Based on Fake Finger Characteristics." International Journal of Digital Crime and Forensics 4, no. 3 (July 2012): 1–19. http://dx.doi.org/10.4018/jdcf.2012070101.
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The vitality detection of fingerprints is currently acknowledged as a serious issue for personal identity verification systems. This problem, raised some years ago, is related to the fact that the 3d shape pattern of a fingerprint can be reproduced using artificial materials. An image quite similar to that of true, alive, fingerprint, is derived if such “fake fingers” are submitted to an electronic scanner. Since introducing hardware dedicated to liveness detection in scanners is expensive, software-based solutions, based on image processing algorithms, have been proposed as alternative. So far, proposed approaches are based on features exploiting characteristics of a live finger (e.g., finger perspiration). Such features can be named live-based, or vitality-based features. In this paper, the authors propose and motivate the use of a novel kind of features exploiting characteristics noticed in the reproduction of fake fingers, that they named fake-based features. Then the authors propose a possibile implementation of this kind of features based on the power spectrum of the fingerprint image. The proposal is compared and integrated with several live-based features at the state-of-the-art, and shows very good liveness detection performances. Experiments are carried out on a data set much larger than commonly adopted ones, containing images from three different optical sensors.
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Arunachalam, N., P. Prabavathy, and S. Priyatharshini. "Hidden Markov Model Based Fault Tolerance in Credit Card Transaction Security." International Journal of Engineering & Technology 7, no. 2.32 (May 31, 2018): 153. http://dx.doi.org/10.14419/ijet.v7i2.32.15392.
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Credit card fake detection has raised unique challenges due to the streaming, imbalanced, and non-stationary nature of the data that has been transacted. It had additionally included an active learning step, since the labeling (fake or genuine) use of a subset on transactions is obtained in near-real time through human investigators contacted the cardholders. In this paper, the Hidden Markov Model (HMM) algorithm has been used for sequence of Credit card operations for transaction processing and the fake can be detected by using the fake detection model during transaction processing. HMM, Fake detection model and image process had played an imperative role in the detection of credit card fake in online transactions. In fake detection, most challenging is a data problem, due to two major reasons – first, the profiles of cardholders are normal and fake lent behaviors changed constantly and secondly, credit card fake data sets are highly changed its position. Using fake detection (FD) algorithm the performance of detection in credit card transactions had highly affected by the sampling approach on dataset, selection of HMM, Fake detection model. Using fake detection (FD) algorithm an image technique had been used. A reliable augmentation of the target scarce population of fakes are important considering issues such as labeling cost; algorithm HMM, fake detection and outlines in the data streamed source. We have approached several scenarios which showed the feasibility of improving detection capabilities evaluated by means of receiver operating characteristic (ROC) curves and several key performance indicators (KPI) commonly used in financial business.
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Ingle, Shunottara. "Detection of Counterfeit Indian Currency." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 20, 2021): 1973–76. http://dx.doi.org/10.22214/ijraset.2021.35341.
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Lot of the fake currency note is being printed in recent years which has caused great loss and damage to society. So, it has become necessary to develop a tool to detect fake currency. This project proposes an approach that will detect fake currency notes being circulated by using their image. Our project will provide required portability and compatibility to most peoples as well as feasible accuracy for fake currency detection. The paper is about Fake Indian Paper Currency using image processing implemented in Android Studio to make the app portable and efficient. Features of currency notes like color, height, width, ratio, watermarks were extracted. The process starts from capturing or browsing the image of a currency note and then compare its features with the real note and check whether it is fake or original.
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Hadi, Wildan Jameel, Suhad Malallah Kadhem, and Ayad Rodhan Abbas. "Fast discrimination of fake video manipulation." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 3 (June 1, 2022): 2582. http://dx.doi.org/10.11591/ijece.v12i3.pp2582-2587.
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<span>Deepfakes have become possible using artificial intelligence techniques, replacing one person’s face with another person’s face (primarily a public figure), making the latter do or say things he would not have done. Therefore, contributing to a solution for video credibility has become a critical goal that we will address in this paper. Our work exploits the visible artifacts (blur inconsistencies) which are generated by the manipulation process. We analyze focus quality and its ability to detect these artifacts. Focus measure operators in this paper include image Laplacian and image gradient groups, which are very fast to compute and do not need a large dataset for training. The results showed that i) the Laplacian group operators, as a value, may be lower or higher in the fake video than its value in the real video, depending on the quality of the fake video, so we cannot use them for deepfake detection and ii) the gradient-based measure (GRA7) decreases its value in the fake video in all cases, whether the fake video is of high or low quality and can help detect deepfake.</span>
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Levine, AB, J. Peng, SJM Jones, A. Bashashati, and S. Yip. "Synthesis of glioma histopathology images using generative adversarial networks." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 48, s1 (May 2021): S3. http://dx.doi.org/10.1017/cjn.2021.91.
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Deep learning, a subset of artificial intelligence, has shown great potential in several recent applications to pathology. These have mainly involved the use of classifiers to diagnose disease, while generative modelling techniques have been less frequently used. Generative adversarial networks (GANs) are a type of deep learning model that has been used to synthesize realistic images in a range of domains, both general purpose and medical. In the GAN framework, a generator network is trained to synthesize fake images, while a dueling discriminator network aims to distinguish between the fake images and a set of real training images. As GAN training progresses, the generator network ideally learns the important features of a dataset, allowing it to create images that the discriminator cannot distinguish from the real ones. We report on our use of GANs to synthesize high resolution, realistic histopathology images of gliomas. The well- known Progressive GAN framework was trained on a set of image patches extracted from digital slides in the Cancer Genome Atlas repository, and was able to generate fake images that were visually indistinguishable from the real training images. Generative modelling in pathology has numerous potential applications, including dataset augmentation for training deep learning classifiers, image processing, and expanding educational material.LEARNING OBJECTIVESThis presentation will enable the learner to: 1.Explain basic principles of generative modelling in deep learning.2.Discuss applications of deep learning to neuropathology image synthesis.
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Zhang, Weiguo, Chenggang Zhao, and Yuxing Li. "A Novel Counterfeit Feature Extraction Technique for Exposing Face-Swap Images Based on Deep Learning and Error Level Analysis." Entropy 22, no. 2 (February 21, 2020): 249. http://dx.doi.org/10.3390/e22020249.
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The quality and efficiency of generating face-swap images have been markedly strengthened by deep learning. For instance, the face-swap manipulations by DeepFake are so real that it is tricky to distinguish authenticity through automatic or manual detection. To augment the efficiency of distinguishing face-swap images generated by DeepFake from real facial ones, a novel counterfeit feature extraction technique was developed based on deep learning and error level analysis (ELA). It is related to entropy and information theory such as cross-entropy loss function in the final softmax layer. The DeepFake algorithm is only able to generate limited resolutions. Therefore, this algorithm results in two different image compression ratios between the fake face area as the foreground and the original area as the background, which would leave distinctive counterfeit traces. Through the ELA method, we can detect whether there are different image compression ratios. Convolution neural network (CNN), one of the representative technologies of deep learning, can extract the counterfeit feature and detect whether images are fake. Experiments show that the training efficiency of the CNN model can be significantly improved by the ELA method. In addition, the proposed technique can accurately extract the counterfeit feature, and therefore achieves outperformance in simplicity and efficiency compared with direct detection methods. Specifically, without loss of accuracy, the amount of computation can be significantly reduced (where the required floating-point computing power is reduced by more than 90%).
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Krishnan., Remya. "IMAGE QUALITY ASSESSMENT FOR FAKE BIOMETRIC DETECTION: APPLICATION TO FINGER-VEIN IMAGES." International Journal of Advanced Research 4, no. 8 (August 31, 2016): 2015–21. http://dx.doi.org/10.21474/ijar01/1418.
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Ghodichor, Prof F. S. "System for Fake Currency Detection Using Image Processing." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 2022): 349–52. http://dx.doi.org/10.22214/ijraset.2022.39832.
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Abstract: Counterfeit money has always existed an issue that has caused many problems in the market. Technological growth development has made it possible to create extra counterfeit items which are distributed in the mitigation market the global economy. Bangui existing banking equipment and so on trading sites to check the authenticity of funds. But the average person does not do that have access to such systems and that is why they are needed in order for the software to receive counterfeit money, which can be used by ordinary people. This the proposed system uses image processing to find out if the money is real or fake. System built uses the Python system completely language. It contains similar steps grayscale modification, edge detection, separation, etc. made using appropriate methods. Keyword: Counterfeit currency, Image Processing, Python programming language, grayscale conversion, edge detection, segmentation.
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ZHAO, Xing-yang, and Ji-yin SUN. "Fake verification analysis of SVD-based image watermarking." Journal of Computer Applications 30, no. 2 (March 23, 2010): 517–20. http://dx.doi.org/10.3724/sp.j.1087.2010.00517.
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