<ul><li>Adversarial attacks pose a critical vulnerability in machine learning models by tricking them using nearly invisible perturbations to images.</li><li>Existing defensive mechanisms for mitigating adversarial attacks often require significant time and computational costs.</li><li>This study presents an improved model that incorporates residual blocks to enhance the generalizability and transferability of the defense method.</li><li>Experimental results demonstrate that the proposed model can restore classification accuracy while maintaining competitive performance compared to state-of-the-art methods.</li></ul>

Leveraging Generalizability of Image-to-Image Translation for Enhanced Adversarial Defense

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