Detection and Mitigation of Deep fakes and AI-Generated Media

Abstract

The rapid advancement of generative artificial intelligence (AI) has resulted in an unprecedented increase in the use of “deep fakes,” highly realistic manipulation of images, videos, and audio content generated by techniques such as Genera-tive Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models. While deepfakes have the po-tential to bring numerous benefits to areas such as entertainment, education, accessibility, and health care, there are many po-tential risks associated with them, including, but not limited to, spreading misinformation or propaganda, identity theft, sabo-taging elections and other government processes, creating unwanted pornography without consent, and reducing the public's faith in digital evidence. This literature review synthesizes the findings from 39 studies on various aspects of deepfakes, in-cluding survey research, experiments, forensic methods, and sociotechnical analyses of both the creation and detection of deepfakes. The main five categories of strategies for detecting deepfakes are: Physiological signal analysis (e.g. eye blinking, inconsistencies in the head pose, lip sync errors etc.). Image and Video Forensic Analysis (e.g. distortions caused by warping, residual noise, and background clues) Deep Learning-Based Detection Techniques (e.g. Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Optical Flow Based Networks and Hybrid Architectures). Hybrid Frameworks and Tool Kits. Human-Centered Detection Techniques, emphasizing the limitations of Human Perception. This review evalu-ates several benchmark data sets used to develop detection research, including DFDC, Celebr-DF, Face Forensics++, DF-Wild, and Deepfake TIMIT. Additionally, this review addresses topics of importance to developing resilience against the in-creasing threat of deep fakes, including, but not limited to, the challenges of achieving Adversarial Robustness, Computa-tional Efficiency, Generalization Gaps, and Dataset Bias. Lastly, the review identifies some of the remaining open problems and possible avenues for future research in multimodal detection, Explainable AI, Lightweight Real-Time Systems, and Regulatory Frameworks.