Relevant bibliographies by topics / Fake image

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Fake image'

Author: Grafiati
Published: 20 February 2023
Last updated: 21 February 2023

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Journal articles on the topic "Fake image"

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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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Dissertations / Theses on the topic "Fake image"

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Wu, Yi. "Artificial-Pulse-Noise based Multipath Suppression Method for Through-Wall Imaging Radar." Thesis, The University of Sydney, 2019. http://hdl.handle.net/2123/19989.

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Through-Wall Radar Imaging (TWRI) uses UWB radar technology to detect the targets behind the wall, complete the panoramic view of the target area after the wall. As a new generation of perspective imaging technology, through-wall radar imaging can be widely used in many fields. However, in through-wall radar imaging, a very commonly seen problem is the fake image. The fake image is caused by the sidewall and back wall reflecting the object signal. The matched-filter results cannot distinguish the fake image from the real image in current research. This thesis proposes a new method to suppress fake images caused by the multipath signal in single target and multi-target scenarios. I consider if adding artificial-pulse-noise(APN) into the received raw radar data，after matched-filter processing，the multipath echoes will be suppressed. This thesis first discusses the multipath radar signal model. By further analyzing the matching filter results of direct path signal and multipath signal with and without APN, it can be theoretically proved that adding APN can suppress the fake image. With the radar data from numerical simulation and experimental results, adding APN has been proved to effectively suppress multipath signals in the matched-filter processing. By using the proposed method in through-wall image algorithm, numerical simulation and experimental results show that the proposed method can suppress the ghost images caused by multi-path reflections from the side and back walls in enclosed rooms in a more effective way. In this thesis, a new TWRI algorithm is proposed to suppress fake images without increasing computational complexity. This means that it is easier to use in reality. Using this method, the existing through-wall radar system can be upgraded, which will make the through-wall radar become more important in anti-terrorism and disaster relief.
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Silva, Simone Faustino da. "Diva, Presidenta e fake: a construÃÃo da imagem de Dilma Rousseff pelo perfil Dilma Bolada no Twitter." Universidade Federal do CearÃ, 2015. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=15389.

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nÃo hÃ
Em um processo contÃnuo de construÃÃo da opiniÃo e moldagem de representaÃÃes nas redes de comunicaÃÃo digital, destaca-se a produÃÃo de conteÃdo e a interaÃÃo realizada por meio da apropriaÃÃo da identidade de terceiros. A cada dia, povoam a internet novos perfis nÃo-oficiais de personalidades conhecidas, estando os atores polÃticos (continuamente sob julgamento pÃblico) especialmente vulnerÃveis a aÃÃes dessa natureza. Em consonÃncia com essa realidade, a presente dissertaÃÃo realiza um estudo qualitativo, ancorado metodologicamente na abordagem da AnÃlise de ConteÃdo (AC) de Bardin (2011) e executado pela prÃpria pesquisadora com o apoio do software Qualitative Solutions Research NVivo em sua versÃo 10.0, facilitador do processo de organizaÃÃo e categorizaÃÃo. Dessa forma, investigou-se a construÃÃo feita da imagem pÃblica da Presidenta Dilma Rousseff na rede social Twitter pelo perfil fake Dilma Bolada, um dos mais populares da internet. A escolha da rede justifica-se pela perspectiva personalizada adotada pela conta âfalsaâ. Este, inclusive, à um caso notÃrio perfil fake cuja visibilidade jà ultrapassa aquela obtida pela conta oficial em algumas redes, como o Facebook e o Instagram. Com base em um recorte temporal que vai de 1 de janeiro atà 5 de julho de 2014 (Ãltimo dia para registro de candidatos a Presidente e Vice, segundo o TSE), foram mapeados no conteÃdo das postagens indÃcios de uma construÃÃo estratÃgica, mÃtica, midiatizada e personalizada da imagem pÃblica, em uma anÃlise relacionada à literatura revisada nos capÃtulos introdutÃrios.
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Linnander, Mathilda. "Last Night in Sweden : A Critical Discourse Analysis of the Image of Sweden in International Media." Thesis, Högskolan i Jönköping, Högskolan för lärande och kommunikation, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-40977.

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This is a study of how the image of Sweden is constructed in international media. Using the country as a swinging bat in debates on socialism and progressiveness is nothing new but has had an upswing during recent years as a result of the global rise of right-wing forces. With the help of Critical Discourse Analysis, four articles from the United States and the United Kingdom are analysed. These are then presented according to Fairclough’s three-layered model. With the help of previous research on Sweden in international media, fake news and nation branding, these findings are then explained and put into context.The study finds that the image of Sweden presented in media tends to follow the narrative of Good Sweden and Bad Sweden. On the one hand is the classic welfare state in the north, which takes care of its people and with high levels of trust between the actors. On the other hand is a country in ruins as a result of letting in too many immigrants. Both narratives rely heavily on stereotypes. The discussion tends to use Sweden as an example, when it is really about ideologies and values. Another result shown by the study is that fake news is a common trace in news about Sweden, not only in alternative media but also in the established elite media. This can be seen as a result of the hardening situation in the media business as well as the rise of right-wing forces.
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Silva, Simone Faustino da. "Diva, Presidenta e fake: a construção da imagem de Dilma Rousseff pelo perfil Dilma Bolada no twitter." www.teses.ufc.br, 2015. http://www.repositorio.ufc.br/handle/riufc/14624.

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SILVA, Simone Faustino da. Diva, Presidenta e fake: a construção da imagem de Dilma Rousseff pelo perfil Dilma Bolada no twitter. 2015. 188f. – Dissertação (Mestrado) – Universidade Federal do Ceará, Programa de Pós-graduação em Comunicação Social, Fortaleza (CE), 2015.
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Em um processo contínuo de construção da opinião e moldagem de representações nas redes de comunicação digital, destaca-se a produção de conteúdo e a interação realizada por meio da apropriação da identidade de terceiros. A cada dia, povoam a internet novos perfis não-oficiais de personalidades conhecidas, estando os atores políticos (continuamente sob julgamento público) especialmente vulneráveis a ações dessa natureza. Em consonância com essa realidade, a presente dissertação realiza um estudo qualitativo, ancorado metodologicamente na abordagem da Análise de Conteúdo (AC) de Bardin (2011) e executado pela própria pesquisadora com o apoio do software Qualitative Solutions Research NVivo em sua versão 10.0, facilitador do processo de organização e categorização. Dessa forma, investigou-se a construção feita da imagem pública da Presidenta Dilma Rousseff na rede social Twitter pelo perfil fake Dilma Bolada, um dos mais populares da internet. A escolha da rede justifica-se pela perspectiva personalizada adotada pela conta “falsa”. Este, inclusive, é um caso notório perfil fake cuja visibilidade já ultrapassa aquela obtida pela conta oficial em algumas redes, como o Facebook e o Instagram. Com base em um recorte temporal que vai de 1º de janeiro até 5 de julho de 2014 (último dia para registro de candidatos a Presidente e Vice, segundo o TSE), foram mapeados no conteúdo das postagens indícios de uma construção estratégica, mítica, midiatizada e personalizada da imagem pública, em uma análise relacionada à literatura revisada nos capítulos introdutórios.
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McIntyre, A. H. "Applying psychology to forensic facial identification : perception and identification of facial composite images and facial image comparison." Thesis, University of Stirling, 2012. http://hdl.handle.net/1893/9077.

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Eyewitness recognition is acknowledged to be prone to error but there is less understanding of difficulty in discriminating unfamiliar faces. This thesis examined the effects of face perception on identification of facial composites, and on unfamiliar face image comparison. Facial composites depict face memories by reconstructing features and configurations to form a likeness. They are generally reconstructed from an unfamiliar face memory, and will be unavoidably flawed. Identification will require perception of any accurate features, by someone who is familiar with the suspect and performance is typically poor. In typical face perception, face images are processed efficiently as complete units of information. Chapter 2 explored the possibility that holistic processing of inaccurate composite configurations will impair identification of individual features. Composites were split below the eyes and misaligned to impair holistic analysis (cf. Young, Hellawell, & Jay, 1987); identification was significantly enhanced, indicating that perceptual expertise with inaccurate configurations exerts powerful effects that can be reduced by enabling featural analysis. Facial composite recognition is difficult, which means that perception and judgement will be influence by an affective recognition bias: smiles enhance perceived familiarity, while negative expressions produce the opposite effect. In applied use, facial composites are generally produced from unpleasant memories and will convey negative expression; affective bias will, therefore, be important for facial composite recognition. Chapter 3 explored the effect of positive expression on composite identification: composite expressions were enhanced, and positive affect significantly increased identification. Affective quality rather than expression strength mediated the effect, with subtle manipulations being very effective. Facial image comparison (FIC) involves discrimination of two or more face images. Accuracy in unfamiliar face matching is typically in the region of 70%, and as discrimination is difficult, may be influenced by affective bias. Chapter 4 explored the smiling face effect in unfamiliar face matching. When multiple items were compared, positive affect did not enhance performance and false positive identification increased. With a delayed matching procedure, identification was not enhanced but in contrast to face recognition and simultaneous matching, positive affect improved rejection of foil images. Distinctive faces are easier to discriminate. Chapter 5 evaluated a systematic caricature transformation as a means to increase distinctiveness and enhance discrimination of unfamiliar faces. Identification of matching face images did not improve, but successful rejection of non-matching items was significantly enhanced. Chapter 6 used face matching to explore the basis of own race bias in face perception. Other race faces were manipulated to show own race facial variation, and own race faces to show African American facial variation. When multiple face images were matched simultaneously, the transformation impaired performance for all of the images; but when images were individually matched, the transformation improved perception of other race faces and discrimination of own race faces declined. Transformation of Japanese faces to show own race dimensions produced the same pattern of effects but failed to reach significance. The results provide support for both perceptual expertise and featural processing theories of own race bias. Results are interpreted with reference to face perception theories; implications for application and future study are discussed.
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Wysocki, Bruna. "Interação face a face: um estudo das estratégias discursivas na reconstrução da imagem." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/8/8142/tde-05052008-153601/.

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O presente trabalho tem por proposta analisar o discurso formulado por um expresidente que pretende reconstruir sua imagem abalada em decorrência do impeachment que sofreu. Ao considerarmos um encontro social, em que os interlocutores interagem face a face, observamos que o interactante procura articular estratégias interacionais, a fim de preservar sua face e protegê-la de eventuais ameaças; ao mesmo tempo, coordena estratégias argumentativas com o intuito de interferir na concepção inicial que seus interlocutores possuem a respeito de sua imagem. Para atingirmos nossos objetivos, consideramos, da Sociolingüística Interacional, as teorias de preservação de faces abordadas por Goffman (1974) e, ao levarmos em conta que estratégias argumentativas também colaboram para a reconstrução da imagem, recorremos aos pressupostos da Teoria da Argumentação, segundo estudos de Perelman e Olbrechts-Tyteca (2002). Com base nesses estudos, partimos para a aplicação dos conceitos em um corpus constituído por uma entrevista televisiva, transmitida pelo Sistema Brasileiro de Televisão (SBT), em agosto de 1998, no \"Programa Livre\", em que o ex-presidente Fernando Collor de Melo é entrevistado por estudantes de ensino médio e cursinho. O corpus foi gravado e transcrito de acordo com as normas publicadas pelo Projeto da Norma Urbana Culta - NURC-SP.
This paper has the purpose of analyzing the discourse delivered by a former president that plans to rebuild his image, since it was shattered as a result of an impeachment sustained by him. Upon considering a social gathering, in which the interlocutors interact face to face, we have noted that the interacting person tries to coordinate interactional strategies in order to preserve his face and protect it against any threats; at the same time, he organizes strategic arguments for the purpose of interfering with the initial assumption that his interlocutors have made in regard to his image. To achieve our goals, we have used, from the Interactional Sociolinguistics, the theory of faces\' preservation as approached by Goffman (1974) and, by taking into account that strategic arguments too cooperate for the reconstruction of the image, we have relied on the assumptions of the Argumentation Theory, according to studies by Perelman & Olbrechts-Tyteca (2002). With basis on this information, we set out to apply the concepts in a corpus made up by a television interview broadcast by SBT, a television network, in August 1998, in the \"Programa Livre\" talk show, in which former President Fernando Collor de Melo was interviewed by high school students. The corpus was recorded and transcribed according to the rules published by Urban Educated Norm Project - NURC - SP
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Mahmood, Muhammad Tariq. "Face Detection by Image Discriminating." Thesis, Blekinge Tekniska Högskola, Avdelningen för för interaktion och systemdesign, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4352.

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Human face recognition systems have gained a considerable attention during last few years. There are very many applications with respect to security, sensitivity and secrecy. Face detection is the most important and first step of recognition system. Human face is non rigid and has very many variations regarding image conditions, size, resolution, poses and rotation. Its accurate and robust detection has been a challenge for the researcher. A number of methods and techniques are proposed but due to a huge number of variations no one technique is much successful for all kinds of faces and images. Some methods are exhibiting good results in certain conditions and others are good with different kinds of images. Image discriminating techniques are widely used for pattern and image analysis. Common discriminating methods are discussed.
SIPL, Mechatronics, GIST 1 Oryong-Dong, Buk-Gu, Gwangju, 500-712 South Korea tel. 0082-62-970-2997
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Youmaran, Richard. "Algorithms to Process and Measure Biometric Information Content in Low Quality Face and Iris Images." Thesis, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/19729.

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Biometric systems allow identification of human persons based on physiological or behavioral characteristics, such as voice, handprint, iris or facial characteristics. The use of face and iris recognition as a way to authenticate user’s identities has been a topic of research for years. Present iris recognition systems require that subjects stand close (<2m) to the imaging camera and look for a period of about three seconds until the data are captured. This cooperative behavior is required in order to capture quality images for accurate recognition. This will eventually restrict the amount of practical applications where iris recognition can be applied, especially in an uncontrolled environment where subjects are not expected to cooperate such as criminals and terrorists, for example. For this reason, this thesis develops a collection of methods to deal with low quality face and iris images and that can be applied for face and iris recognition in a non-cooperative environment. This thesis makes the following main contributions: I. For eye and face tracking in low quality images, a new robust method is developed. The proposed system consists of three parts: face localization, eye detection and eye tracking. This is accomplished using traditional image-based passive techniques such as shape information of the eye and active based methods which exploit the spectral properties of the pupil under IR illumination. The developed method is also tested on underexposed images where the subject shows large head movements. II. For iris recognition, a new technique is developed for accurate iris segmentation in low quality images where a major portion of the iris is occluded. Most existing methods perform generally quite well but tend to overestimate the occluded regions, and thus lose iris information that could be used for identification. This information loss is potentially important in the covert surveillance applications we consider in this thesis. Once the iris region is properly segmented using the developed method, the biometric feature information is calculated for the iris region using the relative entropy technique. Iris biometric feature information is calculated using two different feature decomposition algorithms based on Principal Component Analysis (PCA) and Independent Component Analysis (ICA). III. For face recognition, a new approach is developed to measure biometric feature information and the changes in biometric sample quality resulting from image degradations. A definition of biometric feature information is introduced and an algorithm to measure it proposed, based on a set of population and individual biometric features, as measured by a biometric algorithm under test. Examples of its application were shown for two different face recognition algorithms based on PCA (Eigenface) and Fisher Linear Discriminant (FLD) feature decompositions.
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Tan, Teewoon. "HUMAN FACE RECOGNITION BASED ON FRACTAL IMAGE CODING." University of Sydney. Electrical and Information Engineering, 2004. http://hdl.handle.net/2123/586.

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Human face recognition is an important area in the field of biometrics. It has been an active area of research for several decades, but still remains a challenging problem because of the complexity of the human face. In this thesis we describe fully automatic solutions that can locate faces and then perform identification and verification. We present a solution for face localisation using eye locations. We derive an efficient representation for the decision hyperplane of linear and nonlinear Support Vector Machines (SVMs). For this we introduce the novel concept of $\rho$ and $\eta$ prototypes. The standard formulation for the decision hyperplane is reformulated and expressed in terms of the two prototypes. Different kernels are treated separately to achieve further classification efficiency and to facilitate its adaptation to operate with the fast Fourier transform to achieve fast eye detection. Using the eye locations, we extract and normalise the face for size and in-plane rotations. Our method produces a more efficient representation of the SVM decision hyperplane than the well-known reduced set methods. As a result, our eye detection subsystem is faster and more accurate. The use of fractals and fractal image coding for object recognition has been proposed and used by others. Fractal codes have been used as features for recognition, but we need to take into account the distance between codes, and to ensure the continuity of the parameters of the code. We use a method based on fractal image coding for recognition, which we call the Fractal Neighbour Distance (FND). The FND relies on the Euclidean metric and the uniqueness of the attractor of a fractal code. An advantage of using the FND over fractal codes as features is that we do not have to worry about the uniqueness of, and distance between, codes. We only require the uniqueness of the attractor, which is already an implied property of a properly generated fractal code. Similar methods to the FND have been proposed by others, but what distinguishes our work from the rest is that we investigate the FND in greater detail and use our findings to improve the recognition rate. Our investigations reveal that the FND has some inherent invariance to translation, scale, rotation and changes to illumination. These invariances are image dependent and are affected by fractal encoding parameters. The parameters that have the greatest effect on recognition accuracy are the contrast scaling factor, luminance shift factor and the type of range block partitioning. The contrast scaling factor affect the convergence and eventual convergence rate of a fractal decoding process. We propose a novel method of controlling the convergence rate by altering the contrast scaling factor in a controlled manner, which has not been possible before. This helped us improve the recognition rate because under certain conditions better results are achievable from using a slower rate of convergence. We also investigate the effects of varying the luminance shift factor, and examine three different types of range block partitioning schemes. They are Quad-tree, HV and uniform partitioning. We performed experiments using various face datasets, and the results show that our method indeed performs better than many accepted methods such as eigenfaces. The experiments also show that the FND based classifier increases the separation between classes. The standard FND is further improved by incorporating the use of localised weights. A local search algorithm is introduced to find a best matching local feature using this locally weighted FND. The scores from a set of these locally weighted FND operations are then combined to obtain a global score, which is used as a measure of the similarity between two face images. Each local FND operation possesses the distortion invariant properties described above. Combined with the search procedure, the method has the potential to be invariant to a larger class of non-linear distortions. We also present a set of locally weighted FNDs that concentrate around the upper part of the face encompassing the eyes and nose. This design was motivated by the fact that the region around the eyes has more information for discrimination. Better performance is achieved by using different sets of weights for identification and verification. For facial verification, performance is further improved by using normalised scores and client specific thresholding. In this case, our results are competitive with current state-of-the-art methods, and in some cases outperform all those to which they were compared. For facial identification, under some conditions the weighted FND performs better than the standard FND. However, the weighted FND still has its short comings when some datasets are used, where its performance is not much better than the standard FND. To alleviate this problem we introduce a voting scheme that operates with normalised versions of the weighted FND. Although there are no improvements at lower matching ranks using this method, there are significant improvements for larger matching ranks. Our methods offer advantages over some well-accepted approaches such as eigenfaces, neural networks and those that use statistical learning theory. Some of the advantages are: new faces can be enrolled without re-training involving the whole database; faces can be removed from the database without the need for re-training; there are inherent invariances to face distortions; it is relatively simple to implement; and it is not model-based so there are no model parameters that need to be tweaked.
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Tan, Teewoon. "HUMAN FACE RECOGNITION BASED ON FRACTAL IMAGE CODING." Thesis, The University of Sydney, 2003. http://hdl.handle.net/2123/586.

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Human face recognition is an important area in the field of biometrics. It has been an active area of research for several decades, but still remains a challenging problem because of the complexity of the human face. In this thesis we describe fully automatic solutions that can locate faces and then perform identification and verification. We present a solution for face localisation using eye locations. We derive an efficient representation for the decision hyperplane of linear and nonlinear Support Vector Machines (SVMs). For this we introduce the novel concept of $\rho$ and $\eta$ prototypes. The standard formulation for the decision hyperplane is reformulated and expressed in terms of the two prototypes. Different kernels are treated separately to achieve further classification efficiency and to facilitate its adaptation to operate with the fast Fourier transform to achieve fast eye detection. Using the eye locations, we extract and normalise the face for size and in-plane rotations. Our method produces a more efficient representation of the SVM decision hyperplane than the well-known reduced set methods. As a result, our eye detection subsystem is faster and more accurate. The use of fractals and fractal image coding for object recognition has been proposed and used by others. Fractal codes have been used as features for recognition, but we need to take into account the distance between codes, and to ensure the continuity of the parameters of the code. We use a method based on fractal image coding for recognition, which we call the Fractal Neighbour Distance (FND). The FND relies on the Euclidean metric and the uniqueness of the attractor of a fractal code. An advantage of using the FND over fractal codes as features is that we do not have to worry about the uniqueness of, and distance between, codes. We only require the uniqueness of the attractor, which is already an implied property of a properly generated fractal code. Similar methods to the FND have been proposed by others, but what distinguishes our work from the rest is that we investigate the FND in greater detail and use our findings to improve the recognition rate. Our investigations reveal that the FND has some inherent invariance to translation, scale, rotation and changes to illumination. These invariances are image dependent and are affected by fractal encoding parameters. The parameters that have the greatest effect on recognition accuracy are the contrast scaling factor, luminance shift factor and the type of range block partitioning. The contrast scaling factor affect the convergence and eventual convergence rate of a fractal decoding process. We propose a novel method of controlling the convergence rate by altering the contrast scaling factor in a controlled manner, which has not been possible before. This helped us improve the recognition rate because under certain conditions better results are achievable from using a slower rate of convergence. We also investigate the effects of varying the luminance shift factor, and examine three different types of range block partitioning schemes. They are Quad-tree, HV and uniform partitioning. We performed experiments using various face datasets, and the results show that our method indeed performs better than many accepted methods such as eigenfaces. The experiments also show that the FND based classifier increases the separation between classes. The standard FND is further improved by incorporating the use of localised weights. A local search algorithm is introduced to find a best matching local feature using this locally weighted FND. The scores from a set of these locally weighted FND operations are then combined to obtain a global score, which is used as a measure of the similarity between two face images. Each local FND operation possesses the distortion invariant properties described above. Combined with the search procedure, the method has the potential to be invariant to a larger class of non-linear distortions. We also present a set of locally weighted FNDs that concentrate around the upper part of the face encompassing the eyes and nose. This design was motivated by the fact that the region around the eyes has more information for discrimination. Better performance is achieved by using different sets of weights for identification and verification. For facial verification, performance is further improved by using normalised scores and client specific thresholding. In this case, our results are competitive with current state-of-the-art methods, and in some cases outperform all those to which they were compared. For facial identification, under some conditions the weighted FND performs better than the standard FND. However, the weighted FND still has its short comings when some datasets are used, where its performance is not much better than the standard FND. To alleviate this problem we introduce a voting scheme that operates with normalised versions of the weighted FND. Although there are no improvements at lower matching ranks using this method, there are significant improvements for larger matching ranks. Our methods offer advantages over some well-accepted approaches such as eigenfaces, neural networks and those that use statistical learning theory. Some of the advantages are: new faces can be enrolled without re-training involving the whole database; faces can be removed from the database without the need for re-training; there are inherent invariances to face distortions; it is relatively simple to implement; and it is not model-based so there are no model parameters that need to be tweaked.
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Books on the topic "Fake image"

1

Kemp, Sandra. Future face: Image, identity, innovation. London: Profile Books, 2004.

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Bartlett, Marian Stewart. Face image analysis by unsupervised learning. Boston: Kluwer Academic Publishers, 2001.

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Bartlett, Marian Stewart. Face Image Analysis by Unsupervised Learning. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1637-8.

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Bartlett, Marian Stewart. Face Image Analysis by Unsupervised Learning. Boston, MA: Springer US, 2001.

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The Image of Edessa. Leiden: Brill, 2009.

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Les images d'archives face à l'histoire: De la conservation à la création. Futuroscope: Scéren, 2011.

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Hitler's face: The biography of an image. Philadelphia: University of Pennsylvania Press, 2006.

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Kawulok, Michal, M. Emre Celebi, and Bogdan Smolka, eds. Advances in Face Detection and Facial Image Analysis. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-25958-1.

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1954-, Zhang Yu-Jin, ed. Advances in face image analysis: Techniques and technologies. Hershey, PA: Medical Information Science Reference, 2010.

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Hadyn, Ellis, and Macrae Neil, eds. Validation in psychology. New Brunswick, N.J: Transaction Publishers, 2001.

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Book chapters on the topic "Fake image"

1

Spaziante, Lucio. "Sound, image and fake realism." In Iconic Investigations, 263–74. Amsterdam: John Benjamins Publishing Company, 2013. http://dx.doi.org/10.1075/ill.12.20spa.

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Lubna, Jahanara Islam, and S. M. Abrar Kabir Chowdhury. "Detecting Fake Image: A Review for Stopping Image Manipulation." In Advances in Computational Intelligence, Security and Internet of Things, 146–59. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3666-3_13.

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Lee, Eui Chul, Kang Ryoung Park, and Jaihie Kim. "Fake Iris Detection by Using Purkinje Image." In Advances in Biometrics, 397–403. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11608288_53.

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Lee, Steven Jia He, Tangqing Li, Wynne Hsu, and Mong Li Lee. "Repurpose Image Identification for Fake News Detection." In Lecture Notes in Computer Science, 35–47. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86475-0_4.

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Ngo, Nhat-Khang, and Xuan-Nam Cao. "Pixel-Wise Information in Fake Image Detection." In Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications, 436–43. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-8062-5_30.

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Gogineni, Navyadhara, Yashashvini Rachamallu, Ruchitha Mekala, and H. R. Mamatha. "Fake News Detection on Indian Sources." In Third International Conference on Image Processing and Capsule Networks, 23–35. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12413-6_3.

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Neves, João C., Ruben Tolosana, Ruben Vera-Rodriguez, Vasco Lopes, Hugo Proença, and Julian Fierrez. "GAN Fingerprints in Face Image Synthesis." In Multimedia Forensics, 175–204. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7621-5_8.

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AbstractThe availability of large-scale facial databases, together with the remarkable progresses of deep learning technologies, in particular Generative Adversarial Networks (GANs), have led to the generation of extremely realistic fake facial content, raising obvious concerns about the potential for misuse. Such concerns have fostered the research on manipulation detection methods that, contrary to humans, have already achieved astonishing results in various scenarios. This chapter is focused on the analysis of GAN fingerprints in face image synthesis.
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Li, Yuze, Yaping Zhang, Liangfu Lu, Yongheng Jia, and Jingcheng Liu. "Using Neural Networks for Fake Colorized Image Detection." In Advances in Digital Forensics XV, 201–15. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28752-8_11.

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Majumdar, Puspita, Akshay Agarwal, Mayank Vatsa, and Richa Singh. "Facial Retouching and Alteration Detection." In Handbook of Digital Face Manipulation and Detection, 367–87. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_17.

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AbstractOn the social media platforms, the filters for digital retouching and face beautification have become a common trend. With the availability of easy-to-use image editing tools, the generation of altered images has become an effortless task. Apart from this, advancements in the Generative Adversarial Network (GAN) leads to creation of realistic facial images and alteration of facial images based on the attributes. While the majority of these images are created for fun and beautification purposes, they may be used with malicious intent for negative applications such as deepnude or spreading visual fake news. Therefore, it is important to detect digital alterations in images and videos. This chapter presents a comprehensive survey of existing algorithms for retouched and altered image detection. Further, multiple experiments are performed to highlight the open challenges of alteration detection.
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Nakamura, Kazuaki, Yuto Mori, Naoko Nitta, and Noboru Babaguchi. "Recognizer Cloning Attack on Image Recognition Services and Its Defending Method." In Frontiers in Fake Media Generation and Detection, 235–47. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1524-6_10.

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Conference papers on the topic "Fake image"

1

Edmunds, Taiamiti, and Alice Caplier. "Fake face detection based on radiometric distortions." In 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2016. http://dx.doi.org/10.1109/ipta.2016.7820995.

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Singh, Satyendra, and Rajesh Kumar. "Fake Image Identification using Image Forensic Techniques." In International Conference on Advanced Computing and Software Engineering. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010563000003161.

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Wang, Yonghui, Vahid Zarghami, and Suxia Cui. "Fake Face Detection using Local Binary Pattern and Ensemble Modeling." In 2021 IEEE International Conference on Image Processing (ICIP). IEEE, 2021. http://dx.doi.org/10.1109/icip42928.2021.9506460.

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B, Judy Flavia, Sharnish G, Prashanth Mishra, and Aashirvad Kohli. "Image Denoising for an Efficient Fake Image Identification." In 2022 International Conference on Edge Computing and Applications (ICECAA). IEEE, 2022. http://dx.doi.org/10.1109/icecaa55415.2022.9936093.

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Rana, Dipti P., Simran Bawkar, Mansi Jain, Swati Bothra, and Shailesh Baldaniya. "Image Based Fake Tweet Retrieval (IBFTR)." In 2020 International Conference for Emerging Technology (INCET). IEEE, 2020. http://dx.doi.org/10.1109/incet49848.2020.9154072.

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Giachanou, Anastasia, Guobiao Zhang, and Paolo Rosso. "Multimodal Multi-image Fake News Detection." In 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2020. http://dx.doi.org/10.1109/dsaa49011.2020.00091.

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Tanaka, Miki, and Hitoshi Kiya. "Fake-image detection with Robust Hashing." In 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech). IEEE, 2021. http://dx.doi.org/10.1109/lifetech52111.2021.9391842.

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Masciari, Elio, Vincenzo Moscato, Antonio Picariello, and Giancarlo Sperlí. "Detecting fake news by image analysis." In IDEAS 2020: 24th International Database Engineering & Applications Symposium. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3410566.3410599.

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Latha, L., B. Raajshree, and D. Nivetha. "Fake currency detection using Image processing." In 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA). IEEE, 2021. http://dx.doi.org/10.1109/icaeca52838.2021.9675592.

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Ali-Gombe, Adamu, Eyad Elyan, and Chrisina Jayne. "Multiple Fake Classes GAN for Data Augmentation in Face Image Dataset." In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8851953.

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Reports on the topic "Fake image"

1

Wachs, Brandon. Satellite Image Deep Fake Creation and Detection. Office of Scientific and Technical Information (OSTI), August 2021. http://dx.doi.org/10.2172/1812627.

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Beveridge, J. R., P. J. Phillips, G. H. Givens, B. A. Draper, M. N. Teli, and D. S. Bolme. When high quality face image match poorly. Gaithersburg, MD: National Institute of Standards and Technology, 2011. http://dx.doi.org/10.6028/nist.ir.7759.

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Heisele, Bernd, Tomaso poggio, and Massimilinao Pontil. Face Detection in Still Gray Images. Fort Belvoir, VA: Defense Technical Information Center, May 2000. http://dx.doi.org/10.21236/ada459705.

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Zhao, Bo. Data literacy is our best weapon against fake satellite images. Edited by Sarah Bailey. Monash University, April 2022. http://dx.doi.org/10.54377/10e4-7a77.

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Quinn, George W., and Patrick J. Grother. Performance of face recognition algorithms on compressed images. Gaithersburg, MD: National Institute of Standards and Technology, 2011. http://dx.doi.org/10.6028/nist.ir.7830.

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Grother, Patrick J., George W. Quinn, and P. Jonathon Phillips. Report on the evaluation of 2D still-image face recognition algorithms. Gaithersburg, MD: National Institute of Standards and Technology, 2011. http://dx.doi.org/10.6028/nist.ir.7709.

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Nguyen, N. C., and J. Peraire. An Interpolation Method for the Reconstruction and Recognition of Face Images. Fort Belvoir, VA: Defense Technical Information Center, March 2007. http://dx.doi.org/10.21236/ada471235.

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Тарасова, Олена Юріївна, and Ірина Сергіївна Мінтій. Web application for facial wrinkle recognition. Кривий Ріг, КДПУ, 2022. http://dx.doi.org/10.31812/123456789/7012.

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Facial recognition technology is named one of the main trends of recent years. It’s wide range of applications, such as access control, biometrics, video surveillance and many other interactive humanmachine systems. Facial landmarks can be described as key characteristics of the human face. Commonly found landmarks are, for example, eyes, nose or mouth corners. Analyzing these key points is useful for a variety of computer vision use cases, including biometrics, face tracking, or emotion detection. Different methods produce different facial landmarks. Some methods use only basic facial landmarks, while others bring out more detail. We use 68 facial markup, which is a common format for many datasets. Cloud computing creates all the necessary conditions for the successful implementation of even the most complex tasks. We created a web application using the Django framework, Python language, OpenCv and Dlib libraries to recognize faces in the image. The purpose of our work is to create a software system for face recognition in the photo and identify wrinkles on the face. The algorithm for determining the presence and location of various types of wrinkles and determining their geometric determination on the face is programmed.
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Eastman, Brittany. Legal Issues Facing Automated Vehicles, Facial Recognition, and Privacy Rights. SAE International, July 2022. http://dx.doi.org/10.4271/epr2022016.

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Facial recognition software (FRS) is a form of biometric security that detects a face, analyzes it, converts it to data, and then matches it with images in a database. This technology is currently being used in vehicles for safety and convenience features, such as detecting driver fatigue, ensuring ride share drivers are wearing a face covering, or unlocking the vehicle. Public transportation hubs can also use FRS to identify missing persons, intercept domestic terrorism, deter theft, and achieve other security initiatives. However, biometric data is sensitive and there are numerous remaining questions about how to implement and regulate FRS in a way that maximizes its safety and security potential while simultaneously ensuring individual’s right to privacy, data security, and technology-based equality. Legal Issues Facing Automated Vehicles, Facial Recognition, and Individual Rights seeks to highlight the benefits of using FRS in public and private transportation technology and addresses some of the legitimate concerns regarding its use by private corporations and government entities, including law enforcement, in public transportation hubs and traffic stops. Constitutional questions, including First, Forth, and Ninth Amendment issues, also remain unanswered. FRS is now a permanent part of transportation technology and society; with meaningful legislation and conscious engineering, it can make future transportation safer and more convenient.
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