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Journal articles on the topic 'Adversarial multimedia forensics'

Author: Grafiati

Published: 14 March 2023

Last updated: 31 July 2025

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1

.., Ossama, and Mhmed Algrnaodi. "Deep Learning Fusion for Attack Detection in Internet of Things Communications." Fusion: Practice and Applications 9, no. 2 (2022): 27–47. http://dx.doi.org/10.54216/fpa.090203.

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The increasing deep learning techniques used in multimedia and networkIoT solve many problems and increase performance. Securing the deep learning models, multimedia, and networkIoT has become a major area of research in the past few years which is considered to be a challenge during generative adversarial attacks over the multimedia or networkIoT. Many efforts and studies try to provide intelligent forensics techniques to solve security issues. This paper introduces a holistic organization of intelligent multimedia forensics that involve deep learning fusion, multimedia, and networkIoT forens

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2

Zou, Hao, Pengpeng Yang, Rongrong Ni, and Yao Zhao. "Anti-Forensics of Image Contrast Enhancement Based on Generative Adversarial Network." Security and Communication Networks 2021 (March 24, 2021): 1–8. http://dx.doi.org/10.1155/2021/6663486.

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In the multimedia forensics community, anti-forensics of contrast enhancement (CE) in digital images is an important topic to understand the vulnerability of the corresponding CE forensic method. Some traditional CE anti-forensic methods have demonstrated their effective forging ability to erase forensic fingerprints of the contrast-enhanced image in histogram and even gray level cooccurrence matrix (GLCM), while they ignore the problem that their ways of pixel value changes can expose them in the pixel domain. In this paper, we focus on the study of CE anti-forensics based on Generative Adver

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Wildan, Jameel Hadi, Malallah Kadhem Suhad, and Rodhan Abbas Ayad. "A survey of deepfakes in terms of deep learning and multimedia forensics." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 4 (2022): 4408–14. https://doi.org/10.11591/ijece.v12i4.pp4408-4414.

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Artificial intelligence techniques are reaching us in several forms, some of which are useful but can be exploited in a way that harms us. One of these forms is called deepfakes. Deepfakes is used to completely modify video (or image) content to display something that was not in it originally. The danger of deepfake technology impact on society through the loss of confidence in everything is published. Therefore, in this paper, we focus on deepfake detection technology from the view of two concepts which are deep learning and forensic tools. The purpose of this survey is to give the reader a d

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Naik, Deepak. "Fake Media Forensics:AI – Driven Forensic Analysis of Fake Multimedia Content." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47208.

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Abstract—With the rapid advancement of deep learning techniques, the generation of synthetic media—commonly Research and development on deepfakes technology have reached new levels of sophistication. Digital security along with misinformation face serious threats because of these sophisticated methods. and privacy. Existing deepfake detection models primarily the detection methods primarily analyze either video or audio or image-based forgeries yet they seldom employ unified multi-modal examination methods. The authors introduce here a multi-modal deepfake detection system. The proposed framew

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Amerini, Irene, Mauro Barni, Sebastiano Battiato, et al. "Deepfake Media Forensics: Status and Future Challenges." Journal of Imaging 11, no. 3 (2025): 73. https://doi.org/10.3390/jimaging11030073.

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The rise of AI-generated synthetic media, or deepfakes, has introduced unprecedented opportunities and challenges across various fields, including entertainment, cybersecurity, and digital communication. Using advanced frameworks such as Generative Adversarial Networks (GANs) and Diffusion Models (DMs), deepfakes are capable of producing highly realistic yet fabricated content, while these advancements enable creative and innovative applications, they also pose severe ethical, social, and security risks due to their potential misuse. The proliferation of deepfakes has triggered phenomena like

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6

Maier, Anatol, and Christian Riess. "Reliable Out-of-Distribution Recognition of Synthetic Images." Journal of Imaging 10, no. 5 (2024): 110. http://dx.doi.org/10.3390/jimaging10050110.

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Generative adversarial networks (GANs) and diffusion models (DMs) have revolutionized the creation of synthetically generated but realistic-looking images. Distinguishing such generated images from real camera captures is one of the key tasks in current multimedia forensics research. One particular challenge is the generalization to unseen generators or post-processing. This can be viewed as an issue of handling out-of-distribution inputs. Forensic detectors can be hardened by the extensive augmentation of the training data or specifically tailored networks. Nevertheless, such precautions only

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Muhammad S. Mandisha, Mohamed A. Hussien, Amr K. Shalaby, and Omar M. Fahmy. "Wavelet Transform-based Methods for Forensic Analysis of Digital Images." Journal of Advanced Research in Applied Sciences and Engineering Technology 44, no. 1 (2024): 46–54. http://dx.doi.org/10.37934/araset.44.1.4654.

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In recent years, Generative Adversarial Networks (GANs) have been utilized in many applications of our daily lives to create digital media that was previously impossible. This paper utilizes GANs in multimedia forensics, where the precision and accuracy of image classification are crucial. The objective of the study is to detect sophisticated manipulated images generated by advanced deep learning methods using classical transformation methods. The method combines the classical wavelet transform for feature extraction with a classifier model to distinguish between real and fake images. The prop

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Journal, IJSREM. "Deep Fake Face Detection Using Deep Learning Tech with LSTM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 02 (2024): 1–10. http://dx.doi.org/10.55041/ijsrem28624.

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The fabrication of extremely life like spoof films and pictures that are getting harder to tell apart from actual content is now possible because to the quick advancement of deep fake technology. A number of industries, including cybersecurity, politics, and journalism, are greatly impacted by the widespread use of deepfakes, which seriously jeopardizes the accuracy of digital media. In computer vision, machine learning, and digital forensics, detecting deepfakes has emerged as a crucial topic for study and development. An outline of the most recent cutting-edge methods and difficulties in dee

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Amit, Kapoor, and Vinod Mahor Prof. "Broadcasting Forensics Using Machine Learning Approaches." International Journal of Trend in Scientific Research and Development 7, no. 3 (2023): 1034–45. https://doi.org/10.5281/zenodo.8068297.

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Broadcasting forensic is the practice of using scientific methods and techniques to analyse and authenticate Multimedia content. Over the past decade, consumer-grade imaging sensors have become increasingly prevalent, generating vast quantities of images and videos that are used for various public and private communication purposes. Such applications include publicity, advocacy, disinformation, and deception, among others. This paper aims to develop tools that can extract knowledge from these visuals and comprehend their provenance. However, many images and videos undergo modification and mani

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Pankhuri, Tripathi, Singh Shikha, Nishad Anubhav, and Siddiqui Farheen. "Fraudshield – Deepfake Detection Tools." International Journal of Engineering and Management Research 15, no. 2 (2025): 47–51. https://doi.org/10.5281/zenodo.15314707.

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FraudShield is a web application designed to detect and mitigate the impact of deepfakes, ensuring content authenticity and integrity. With the rise of image manipulation and deepfake videos, detecting fraudulent activities has become increasingly critical. This project introduces a hybrid detection system that integrates Convolutional Neural Networks (CNNs) to identify morphed images and manipulated content. The framework leverages machine learning techniques to detect tampered facial features, artifacts, and inconsistencies in deepfake videos and images. The CNN component analyzes visual fea

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11

Byeon, Haewon, Mohammad Shabaz, Kapil Shrivastava, et al. "Deep learning model to detect deceptive generative adversarial network generated images using multimedia forensic." Computers and Electrical Engineering 113 (January 2024): 109024. http://dx.doi.org/10.1016/j.compeleceng.2023.109024.

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Savitha, A. C., Kumar KM Madhu, M. Pallavi, Chincholi Pallavi, H. B. Prethi, and Rachitha. "Experimental Detection of Deep Fake Images Using Face Swap Algorithm." Journal of Scholastic Engineering Science and Management (JSESM), A Peer Reviewed Refereed Multidisciplinary Research Journal 4, no. 5 (2025): 56–61. https://doi.org/10.5281/zenodo.15397033.

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Deepfakes enable highly realistic face-swapping in videos using deep learning. To address the threat posed by Deepfakes, the DFDC dataset, the largest face-swapped video dataset to date, was created with over 100,000 clips generated using multiple methods, including Deepfake Autoencoders and GANs. The dataset consists of videos from 3,426 consenting actors. It supports the development of scalable Deepfake detection models and includes a public Kaggle competition to benchmark solutions. The dataset highlights the complexity of Deepfake detection but shows the potential for generalization to rea

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Vyas, Ritesh, Michele Nappi, Alberto del Bimbo, and Sambit Bakshi. "Introduction to Special Issue on “Recent trends in Multimedia Forensics”." ACM Transactions on Multimedia Computing, Communications, and Applications, August 2, 2024. http://dx.doi.org/10.1145/3678473.

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Multimedia forensics is a subject area which is the need of the hour in this modern era of media-manipulation and generation of fake images/videos assisted with artificial intelligence (AI) models. With the ubiquitous expansion of internet enabled devices, there is a humungous amount of data available to the perusal of forensic experts. This data comprises of audio, video, images, text or a mix of those. Hence multimedia forensics, which involves a set of scientific techniques to collect, scrutinize and analyze this digital content, becomes highly imperative. The increasing threat of compellin

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14

Capasso, Paola, Giuseppe Cattaneo, and Maria De Marsico. "A Comprehensive Survey on Methods for Image Integrity." ACM Transactions on Multimedia Computing, Communications, and Applications, November 16, 2023. http://dx.doi.org/10.1145/3633203.

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The outbreak of digital devices on the Internet, the exponential diffusion of data (images, video, audio, and text), along with their manipulation/generation also by Artificial Intelligence (AI) models, e.g., Generative Adversarial Networks (GANs), have created a great deal of concern in the field of forensics. A malicious use can affect relevant application domains, which often include counterfeiting biomedical images, and deceiving biometric authentication systems, as well as their use in scientific publications, in the political world, and even in school activities. It has been demonstrated

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15

Hemanth Chandra N, Nikhitha S Naik, and Sameeksha A B. "Deepfake Creation and Detection of Multimedia Data." International Journal of Advanced Research in Science, Communication and Technology, February 4, 2024, 36–41. http://dx.doi.org/10.48175/ijarsct-15308.

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As the increasing number of deepfake content poses a growing threat to multimedia integrity, this paper proposes a robust deepfake detection approach based on a hybrid architecture. The proposed framework combines the power of Residual Networks (ResNet) for spatial feature extraction and Long Short-Term Memory (LSTM) with Convolutional Neural Networks (CNN) for temporal dependency modeling. The ResNet component captures complicated patterns in facial and contextual information, whereas the LSTM-CNN module identifies dynamic facial expressions and movements across multiple frames. Transfer lear

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"DEEPFAKE DYSTOPIA: NAVIGATING THE LANDSCAPE OF THREATS AND SAFEGUARDS IN MULTIMEDIA CONTENT." International Journal of Trendy Research in Engineering and Technology 08, no. 01 (2024): 01–07. http://dx.doi.org/10.54473/ijtret.2024.8101.

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Deepfake technology, a fusion of deep learning and artificial intelligence, has emerged as a potent tool capable of crafting hyper-realistic yet entirely fabricated multimedia content. This comprehensive review explores the evolution, applications, and underlying principles of deepfake technology, emphasizing its potential implications for privacy, security, and the spread of misinformation. Using advanced deep learning algorithms, particularly Generative Adversarial Networks (GANs), deepfake technology manipulates facial features with remarkable precision, raising concerns about its malicious

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17

Zhang, Jian, Jiangqun Ni, Fan Nie, and jiwu Huang. "Domain-invariant and Patch-discriminative Feature Learning for General Deepfake Detection." ACM Transactions on Multimedia Computing, Communications, and Applications, April 27, 2024. http://dx.doi.org/10.1145/3657297.

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Hyper-realistic avatars in the metaverse have already raised security concerns about deepfake techniques, deepfakes involving generated video “recording” may be mistaken for a real recording of the people it depicts. As a result, deepfake detection has drawn considerable attention in the multimedia forensic community. Though existing methods for deepfake detection achieve fairly good performance under the intra-dataset scenario, many of them gain unsatisfying results in the case of cross-dataset testing with more practical value, where the forged faces in training and testing datasets are from

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18

Guarnera, Luca, Oliver Giudice, and Sebastiano Battiato. "Mastering Deepfake Detection: A Cutting-Edge Approach to Distinguish GAN and Diffusion-Model Images." ACM Transactions on Multimedia Computing, Communications, and Applications, March 9, 2024. http://dx.doi.org/10.1145/3652027.

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Detecting and recognizing deepfakes is a pressing issue in the digital age. In this study, we first collected a dataset of pristine images and fake ones properly generated by nine different Generative Adversarial Network (GAN) architectures and four Diffusion Models (DM). The dataset contained a total of 83,000 images, with equal distribution between the real and deepfake data. Then, to address different deepfake detection and recognition tasks, we proposed a hierarchical multi-level approach. At the first level, we classified real images from AI-generated ones. At the second level, we disting

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