Methods and Trends in Detecting Generated Images: A Comprehensive Review

• URL: http://arxiv.org/abs/2502.15176v1
• Date: Fri, 21 Feb 2025 03:16:18 GMT
• Title: Methods and Trends in Detecting Generated Images: A Comprehensive Review
• Authors: Arpan Mahara, Naphtali Rishe,
• Abstract summary: Generative Adversarial Networks (GANs), Diffusion Models, and Variational Autoencoders (VAEs) have enabled the synthesis of high-quality multimedia data.<n>These advancements have also raised significant concerns regarding adversarial attacks, unethical usage, and societal harm.
• Score: 0.552480439325792
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The proliferation of generative models, such as Generative Adversarial Networks (GANs), Diffusion Models, and Variational Autoencoders (VAEs), has enabled the synthesis of high-quality multimedia data. However, these advancements have also raised significant concerns regarding adversarial attacks, unethical usage, and societal harm. Recognizing these challenges, researchers have increasingly focused on developing methodologies to detect synthesized data effectively, aiming to mitigate potential risks. Prior reviews have primarily focused on deepfake detection and often lack coverage of recent advancements in synthetic image detection, particularly methods leveraging multimodal frameworks for improved forensic analysis. To address this gap, the present survey provides a comprehensive review of state-of-the-art methods for detecting and classifying synthetic images generated by advanced generative AI models. This review systematically examines core detection methodologies, identifies commonalities among approaches, and categorizes them into meaningful taxonomies. Furthermore, given the crucial role of large-scale datasets in this field, we present an overview of publicly available datasets that facilitate further research and benchmarking in synthetic data detection.

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