Christos Korgialas, Gabriel Lee Jun Rong, Dion Jia Xu Ho +3 more
Deepfake detectors have a soft spot: they often key on brittle, model-specific artifacts, so a doctored image that fools one detector may sail past another it was never tuned against. That is the black-box transfer problem, and it gets worse going from convolutional detectors to transformer-based ones, which trip on different cues. ARMOR++ is an attack framework built to exploit that gap, and it reads as a warning about how fragile these detectors still are.
It uses two language-and-vision models as a control layer: one supplies a sense of where in the image to focus, the other orchestrates a toolbox of five perturbation methods (dense optimization, saliency tweaks, spatial warps, frequency-domain changes, and block-level edits), tuning and mixing them on the fly. Because different detectors have different blind spots, hitting several at once transfers better. On the AADD-2025 benchmark they report higher blind-target success than prior attacks.
This summary is from the abstract, so the paper holds the exact success rates and the defensive settings they tested against.
The reliability of deepfake detectors frequently degrades under black-box adversarial transfer, as these models often rely on fragile, architecture-dependent forensic cues. Existing transfer attacks often lack semantic awareness and struggle to maintain effectiveness under strict no-query constraints, particularly when perturbations are transferred from convolutional surrogates to transformer-based targets. To address these limitations, this paper introduces ARMOR++, a…
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