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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2510.12468 |
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| _version_ | 1866917012163264512 |
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| author | Ho, Dion J. X. Rong, Gabriel Lee Jun Shrivastava, Niharika Abichandani, Harshavardhan Ng, Pai Chet Miao, Xiaoxiao |
| author_facet | Ho, Dion J. X. Rong, Gabriel Lee Jun Shrivastava, Niharika Abichandani, Harshavardhan Ng, Pai Chet Miao, Xiaoxiao |
| contents | We present MS-GAGA (Metric-Selective Guided Adversarial Generation Attack), a two-stage framework for crafting transferable and visually imperceptible adversarial examples against deepfake detectors in black-box settings. In Stage 1, a dual-stream attack module generates adversarial candidates: MNTD-PGD applies enhanced gradient calculations optimized for small perturbation budgets, while SG-PGD focuses perturbations on visually salient regions. This complementary design expands the adversarial search space and improves transferability across unseen models. In Stage 2, a metric-aware selection module evaluates candidates based on both their success against black-box models and their structural similarity (SSIM) to the original image. By jointly optimizing transferability and imperceptibility, MS-GAGA achieves up to 27% higher misclassification rates on unseen detectors compared to state-of-the-art attacks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_12468 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MS-GAGA: Metric-Selective Guided Adversarial Generation Attack Ho, Dion J. X. Rong, Gabriel Lee Jun Shrivastava, Niharika Abichandani, Harshavardhan Ng, Pai Chet Miao, Xiaoxiao Computer Vision and Pattern Recognition We present MS-GAGA (Metric-Selective Guided Adversarial Generation Attack), a two-stage framework for crafting transferable and visually imperceptible adversarial examples against deepfake detectors in black-box settings. In Stage 1, a dual-stream attack module generates adversarial candidates: MNTD-PGD applies enhanced gradient calculations optimized for small perturbation budgets, while SG-PGD focuses perturbations on visually salient regions. This complementary design expands the adversarial search space and improves transferability across unseen models. In Stage 2, a metric-aware selection module evaluates candidates based on both their success against black-box models and their structural similarity (SSIM) to the original image. By jointly optimizing transferability and imperceptibility, MS-GAGA achieves up to 27% higher misclassification rates on unseen detectors compared to state-of-the-art attacks. |
| title | MS-GAGA: Metric-Selective Guided Adversarial Generation Attack |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.12468 |