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arXiv Computer Vision By Jannatul Masruk Mukta, Rifa Sanjida, Adrita Rahman Tory, Md. Saifur Rahman, Khondokar Fida Hasan

Preprocessing Failure and Adversarial Detection in Depthwise-Separable Edge Vision Systems

Read the original on arXiv Computer Vision →

The paper evaluates six preprocessing defenses against adversarial attacks on depthwise‑separable CNNs, the dominant architecture in edge vision systems, and finds that these defenses consistently fail to recover clean predictions for such models, whereas a residual architecture shows partial recovery. The study reveals that the same preprocessing steps that break clean predictions leave adversarial predictions largely intact, creating a measurable asymmetry that can be exploited for detection without retraining or architectural changes. It also demonstrates that common image quality metrics do not reliably indicate defense effectiveness, highlighting a methodological gap in current evaluation practices.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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