Unmasking Synthetic Realities in Generative AI: A Comprehensive Review of Adversarially Robust Deepfake Detection Systems
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866913963869995008 |
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| author | Khan, Naseem Nguyen, Tuan Bermak, Amine Khalil, Issa |
| author_facet | Khan, Naseem Nguyen, Tuan Bermak, Amine Khalil, Issa |
| contents | The rapid advancement of Generative Artificial Intelligence has fueled deepfake proliferation-synthetic media encompassing fully generated content and subtly edited authentic material-posing challenges to digital security, misinformation mitigation, and identity preservation. This systematic review evaluates state-of-the-art deepfake detection methodologies, emphasizing reproducible implementations for transparency and validation. We delineate two core paradigms: (1) detection of fully synthetic media leveraging statistical anomalies and hierarchical feature extraction, and (2) localization of manipulated regions within authentic content employing multi-modal cues such as visual artifacts and temporal inconsistencies. These approaches, spanning uni-modal and multi-modal frameworks, demonstrate notable precision and adaptability in controlled settings, effectively identifying manipulations through advanced learning techniques and cross-modal fusion. However, comprehensive assessment reveals insufficient evaluation of adversarial robustness across both paradigms. Current methods exhibit vulnerability to adversarial perturbations-subtle alterations designed to evade detection-undermining reliability in real-world adversarial contexts. This gap highlights critical disconnect between methodological development and evolving threat landscapes. To address this, we contribute a curated GitHub repository aggregating open-source implementations, enabling replication and testing. Our findings emphasize urgent need for future work prioritizing adversarial resilience, advocating scalable, modality-agnostic architectures capable of withstanding sophisticated manipulations. This review synthesizes strengths and shortcomings of contemporary deepfake detection while charting paths toward robust trustworthy systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21157 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unmasking Synthetic Realities in Generative AI: A Comprehensive Review of Adversarially Robust Deepfake Detection Systems Khan, Naseem Nguyen, Tuan Bermak, Amine Khalil, Issa Cryptography and Security Computer Vision and Pattern Recognition F.2.2; I.2.7 The rapid advancement of Generative Artificial Intelligence has fueled deepfake proliferation-synthetic media encompassing fully generated content and subtly edited authentic material-posing challenges to digital security, misinformation mitigation, and identity preservation. This systematic review evaluates state-of-the-art deepfake detection methodologies, emphasizing reproducible implementations for transparency and validation. We delineate two core paradigms: (1) detection of fully synthetic media leveraging statistical anomalies and hierarchical feature extraction, and (2) localization of manipulated regions within authentic content employing multi-modal cues such as visual artifacts and temporal inconsistencies. These approaches, spanning uni-modal and multi-modal frameworks, demonstrate notable precision and adaptability in controlled settings, effectively identifying manipulations through advanced learning techniques and cross-modal fusion. However, comprehensive assessment reveals insufficient evaluation of adversarial robustness across both paradigms. Current methods exhibit vulnerability to adversarial perturbations-subtle alterations designed to evade detection-undermining reliability in real-world adversarial contexts. This gap highlights critical disconnect between methodological development and evolving threat landscapes. To address this, we contribute a curated GitHub repository aggregating open-source implementations, enabling replication and testing. Our findings emphasize urgent need for future work prioritizing adversarial resilience, advocating scalable, modality-agnostic architectures capable of withstanding sophisticated manipulations. This review synthesizes strengths and shortcomings of contemporary deepfake detection while charting paths toward robust trustworthy systems. |
| title | Unmasking Synthetic Realities in Generative AI: A Comprehensive Review of Adversarially Robust Deepfake Detection Systems |
| topic | Cryptography and Security Computer Vision and Pattern Recognition F.2.2; I.2.7 |
| url | https://arxiv.org/abs/2507.21157 |