# Image Super-Resolution as a Defense Against Adversarial Attacks Source: [Image Super-Resolution as a Defense Against Adversarial Attacks]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:01:21.007Z Authors: Aamir Mustafa, Salman H. Khan, Munawar Hayat, Jianbing Shen, Ling Shao Published: 2019-01-07T06:43:23.000Z ## Abstract Convolutional Neural Networks have achieved significant success across multiple computer vision tasks. However, they are vulnerable to carefully crafted, human-imperceptible adversarial noise patterns which constrain their deployment in critical security-sensitive systems. This paper proposes a computationally efficient image enhancement approach that provides a strong defense mechanism to effectively mitigate the effect of such adversarial perturbations. We show that deep image restoration networks learn mapping functions that can bring off-the-manifold adversarial samples onto the natural image manifold, thus restoring classification towards correct classes. A distinguishing feature of our approach is that, in addition to providing robustness against attacks, it simultaneously enhances image quality and retains models performance on clean images. Furthermore, the proposed method does not modify the classifier or requires a separate mechanism to detect adversarial images. The effectiveness of the scheme has been demonstrated through extensive experiments, where it has proven a strong defense in gray-box settings. The proposed scheme is simple and has the following advantages: (1) it does not require any model training or parameter optimization, (2) it complements other existing defense mechanisms, (3) it is agnostic to the attacked model and attack type and (4) it provides superior performance across all popular attack algorithms. Our codes are publicly available at https://github.com/aamir-mustafa/super-resolution-adversarial-defense. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Input Image Reconstruction on Deepfake Reconstruction Dataset](): Input Image Reconstruction; Deepfake Reconstruction Dataset; MSE: 15.53. - [Output Image Reconstruction on Deepfake Reconstruction Dataset](): Output Image Reconstruction; Deepfake Reconstruction Dataset; MSE: 30.6. ## Original sources and implementation [Original paper]() [PDF]()