Omslagsbillede

Detecting and Corrupting Convolution-based Unlearnable Examples

Guardado en:
Udgivet i:arXiv.org (Dec 10, 2024), p. n/a
Hovedforfatter: Li, Minghui
Andre forfattere: Wang, Xianlong, Yu, Zhifei, Hu, Shengshan, Zhou, Ziqi, Zhang, Longling, Leo Yu Zhang
Udgivet:
Cornell University Library, arXiv.org
Fag:
Datasets
Image compression
Pixels
Convolution
Interpolation
Training
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Resumen:Convolution-based unlearnable examples (UEs) employ class-wise multiplicative convolutional noise to training samples, severely compromising model performance. This fire-new type of UEs have successfully countered all defense mechanisms against UEs. The failure of such defenses can be attributed to the absence of norm constraints on convolutional noise, leading to severe blurring of image features. To address this, we first design an Edge Pixel-based Detector (EPD) to identify convolution-based UEs. Upon detection of them, we propose the first defense scheme against convolution-based UEs, COrrupting these samples via random matrix multiplication by employing bilinear INterpolation (COIN) such that disrupting the distribution of class-wise multiplicative noise. To evaluate the generalization of our proposed COIN, we newly design two convolution-based UEs called VUDA and HUDA to expand the scope of convolution-based UEs. Extensive experiments demonstrate the effectiveness of detection scheme EPD and that our defense COIN outperforms 11 state-of-the-art (SOTA) defenses, achieving a significant improvement on the CIFAR and ImageNet datasets.
ISSN:2331-8422
Fuente:Engineering Database