Abstract: The pervasive emergence of deepfake technology presents unprecedented challenges to the authenticity of digital imagery, prompting the need for advanced methods in detection and mitigation. This review synthesizes insights from multiple pivotal papers, spanning diverse approaches to image forgery detection. It begins with an exploration of the intricacies and societal ramifications of deepfake technology. Navigating through methodologies like CNN-based passive tamper detection, block-based copy-move forgery detection, and ensemble approaches using advanced neural network architectures such as Inception Resnet V2, the review scrutinizes each method's distinctive strengths and limitations, providing a nuanced understanding of their efficacy against digital image manipulations. A comparative analysis reveals the variances and trade-offs inherent in these detection methodologies, offering a valuable resource for researchers and practitioners in image forensics. The abstract concludes by outlining persisting challenges in image forgery detection and suggesting prospective avenues for future research. By distilling a comprehensive overview of contemporary image forensics, this review equips stakeholders with essential insights to navigate the evolving landscape of digital image authenticity and fortify defenses against the escalating threat of deepfake manipulations.
Keywords: deepfake, image forensics, CNN, forgery detection, challenges in image forensics.
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DOI:
10.17148/IJARCCE.2024.134191
