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arXiv · 2510.12468

MS-GAGA: Metric-Selective Guided Adversarial Generation Attack

Dion J. X. Ho·Gabriel Lee Jun Rong·Niharika Shrivastava·Harshavardhan Abichandani·Pai Chet Ng·Xiaoxiao Miao
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Abstract

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.

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Dion J. X. Ho, Gabriel Lee Jun Rong, Niharika Shrivastava, Harshavardhan Abichandani, Pai Chet Ng, Xiaoxiao Miao. 2025-10-14. MS-GAGA: Metric-Selective Guided Adversarial Generation Attack. https://arxiv.org/abs/2510.12468

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