Deflecting Adversarial Attacks with Pixel Deflection (1801.08926v3)

Abstract: CNNs are poised to become integral parts of many critical systems. Despite their robustness to natural variations, image pixel values can be manipulated, via small, carefully crafted, imperceptible perturbations, to cause a model to misclassify images. We present an algorithm to process an image so that classification accuracy is significantly preserved in the presence of such adversarial manipulations. Image classifiers tend to be robust to natural noise, and adversarial attacks tend to be agnostic to object location. These observations motivate our strategy, which leverages model robustness to defend against adversarial perturbations by forcing the image to match natural image statistics. Our algorithm locally corrupts the image by redistributing pixel values via a process we term pixel deflection. A subsequent wavelet-based denoising operation softens this corruption, as well as some of the adversarial changes. We demonstrate experimentally that the combination of these techniques enables the effective recovery of the true class, against a variety of robust attacks. Our results compare favorably with current state-of-the-art defenses, without requiring retraining or modifying the CNN.

Overview of "LaTeX Author Guidelines for CVPR Proceedings"

The document titled "LaTeX Author Guidelines for CVPR Proceedings" is a comprehensive guide tailored for authors preparing their manuscripts for submission to the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). This paper elucidates the essential formatting requirements and submission policies that authors must adhere to for proper alignment with the conference's editorial standards.

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Key Technical Specifications

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• Two-Column Format: All text must be formatted in two columns with precise measurements for width and spacing, ensuring uniformity across submissions.
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Speculation on Future Developments

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In conclusion, the "LaTeX Author Guidelines for CVPR Proceedings" is a detailed manual that assists authors in producing coherent and professionally formatted submissions. By adhering to these guidelines, authors contribute to the overall quality and accessibility of research presented at the esteemed CVPR conference, facilitating the advancement of computer vision research and discourse.