ISPCloak: Weaponizing ISP for Optimization-Free Physical Camouflage against Deepfake Detectors
This paper presents ISPCloak, an optimization-free adversarial attack framework that leverages hardware-intrinsic statistical signatures from image signal processing (ISP) pipelines to cloak AI-generated images as authentic photographs, effectively bypassing current deepfake detectors. By projecting images into the RAW domain, injecting realistic sensor noise, and reconstructing the ISP pipeline, the method generates imperceptible adversarial examples that universally deceive detection mechanisms.
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[Submitted on 24 Jul 2026]
Title:ISPCloak: Weaponizing ISP for Optimization-Free Physical Camouflage against Deepfake Detectors
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Abstract:The rapid advancement of generative models has spurred the critical need to evaluate the worst-case robustness of deepfake detectors. In this paper, we reveal a fundamental blind spot in current forensic paradigms: while existing detectors excel at capturing digital synthesis artifacts, their effectiveness drops drastically when AI-generated content is cloaked in authentic physical imaging characteristics. We posit that genuine photographs inherently possess hardware-intrinsic statistical signatures, which are imperceptible footprints imprinted by optical sensors and Image Signal Processing (ISP) pipelines, and are fundamentally absent in purely data-driven generative models. Driven by this insight, we propose ISPCloak, a novel optimization-free adversarial attack framework that explicitly weaponizes the ISP pipeline to mislead the judgment of deepfake detectors. Rather than relying on computationally expensive gradient perturbations, our method first employs an Invertible ISP network to project images into the RAW domain. Then, we seamlessly imprint the complex statistical priors of real cameras onto AI-generated images by injecting realistic Poisson-Gaussian sensor noise and conducting forward ISP reconstruction. Synergized with generative artifact suppression and adaptive masking, this streamlined physical simulation enables ultra-fast generation of adversarial examples. Extensive experiments show that embedding authentic physical perturbations fundamentally disrupts a broad range of current detection mechanisms, yielding universally evasive adversarial examples with imperceptible visual alterations.
Comments: Accpted by ACM MM 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
Cite as: arXiv:2607.21897 [cs.CV]
(or arXiv:2607.21897v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.21897
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: JingHui Qin [view email] [v1] Fri, 24 Jul 2026 01:59:53 UTC (695 KB)
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