← Back to all news
Hugging Face Trending Papers

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

Read the original on Hugging Face Trending Papers →

The paper examines how preprocessing defenses, commonly used to protect edge vision systems, perform on depthwise‑separable CNNs versus residual architectures. Six preprocessing methods were tested against adversarial attacks, revealing that depthwise‑separable models consistently fail to recover from perturbations while residual models show partial recovery. Interestingly, the same preprocessing that hinders clean predictions leaves adversarial predictions largely intact, offering a measurable detection signal, and the study also finds that typical image‑quality metrics do not reliably indicate defense success.

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 Hugging Face Trending Papers.

Related stories

arXiv Computer Vision
Sep 4

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

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.

By Jannatul Masruk Mukta, Rifa Sanjida, Adrita Rahman Tory, Md. Saifur Rahman, Khondokar Fida Hasan
benchmarkssafety
More like this →
Hugging Face Trending Papers
Aug 5

A Survey of Adversarial Efficiency Degradation for Vision Transformer by Exploiting Input-adaptive Optimization

Vision Transformers (ViTs) increasingly rely on input-adaptive inference, such as token pruning and early halting, to meet energy and latency budgets. This survey examines a recent class of adversarial efficiency degradation attacks that target these mechanisms to increase computation without necessarily degrading accuracy.

llmscomputer-visionefficiencysafety
More like this →
arXiv AI
Jun 11

Diffusion-based Cumulative Adversarial Purification for Vision Language Models

arXiv:2506. 03933v2 Announce Type: replace-cross Abstract: Vision Language Models (VLMs) have shown remarkable capabilities in multimodal understanding, yet their susceptibility to adversarial perturbations poses a significant threat to their reliability in real-world applications.

By Jia Fu, Yongtao Wu, Yihang Chen, Kunyu Peng, Xiao Zhang, Volkan Cevher, Sepideh Pashami, Anders Holst
llmsragdiffusionmultimodalsafety
More like this →