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GraphRectify: Graph-Based Transfer of Adversarial Example Detectors Across Neural Networks

Paper recorded by Signals 4 on 2026-10-07 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-07 on arXiv · recorded by Signals 4 on 2026-10-08

Category: cs.CV · 计算机视觉 · first seen 2026-10-08

Abstract

Adversarial example detectors are often tied to the classifier backbone they were trained on, limiting reuse when the protected model is replaced or upgraded. Directly transferring such detectors across backbones is challenging because different networks generally produce incompatible internal representations. We propose GraphRectify, a graph-based framework for transferring adversarial image dete

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#329 most recent of 756 cs.CV papers we have recorded · ↑ newer: SGF+: Decoupling Gradient Flows for Autoregressive Video Generation · ↓ older: Rubix: Global Correspondence-Free Point Set Alignment through Assignme
Cite this page: GraphRectify: Graph-Based Transfer of Adversarial Example Detectors Across Neural Networks: the #329 most recent of 756 cs.CV papers we have recorded (as of 2026-10-07). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/graphrectify-graph-based-transfer-of-adversarial-example-detectors-across-neural.html
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