Class-Conditioned Transformation for Enhanced Robust Image Classification

• URL: http://arxiv.org/abs/2303.15409v2
• Date: Mon, 04 Nov 2024 19:05:51 GMT
• Title: Class-Conditioned Transformation for Enhanced Robust Image Classification
• Authors: Tsachi Blau, Roy Ganz, Chaim Baskin, Michael Elad, Alex M. Bronstein,
• Abstract summary: We propose a novel test-time threat model algorithm that enhances Adrial-versa-Trained (AT) models. Our method operates through COnditional image transformation and DIstance-based Prediction (CODIP) The proposed method achieves state-of-the-art results demonstrated through extensive experiments on various models, AT methods, datasets, and attack types.
• Score: 19.738635819545554
• License:
• Abstract: Robust classification methods predominantly concentrate on algorithms that address a specific threat model, resulting in ineffective defenses against other threat models. Real-world applications are exposed to this vulnerability, as malicious attackers might exploit alternative threat models. In this work, we propose a novel test-time threat model agnostic algorithm that enhances Adversarial-Trained (AT) models. Our method operates through COnditional image transformation and DIstance-based Prediction (CODIP) and includes two main steps: First, we transform the input image into each dataset class, where the input image might be either clean or attacked. Next, we make a prediction based on the shortest transformed distance. The conditional transformation utilizes the perceptually aligned gradients property possessed by AT models and, as a result, eliminates the need for additional models or additional training. Moreover, it allows users to choose the desired balance between clean and robust accuracy without training. The proposed method achieves state-of-the-art results demonstrated through extensive experiments on various models, AT methods, datasets, and attack types. Notably, applying CODIP leads to substantial robust accuracy improvement of up to $+23\%$, $+20\%$, $+26\%$, and $+22\%$ on CIFAR10, CIFAR100, ImageNet and Flowers datasets, respectively.

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