Towards Robust Classification with Image Quality Assessment
This addresses robustness issues for DCNN-based applications in critical tasks, but it is incremental as it builds on existing defense strategies.
The paper tackles the problem of deep convolutional neural networks being vulnerable to adversarial examples and sensitive to image quality by proposing a protective mechanism that combines image quality assessment with knowledge distillation to detect such inputs without retraining, demonstrating effectiveness on a ResNet model trained on ImageNet.
Recent studies have shown that deep convolutional neural networks (DCNN) are vulnerable to adversarial examples and sensitive to perceptual quality as well as the acquisition condition of images. These findings raise a big concern for the adoption of DCNN-based applications for critical tasks. In the literature, various defense strategies have been introduced to increase the robustness of DCNN, including re-training an entire model with benign noise injection, adversarial examples, or adding extra layers. In this paper, we investigate the connection between adversarial manipulation and image quality, subsequently propose a protective mechanism that doesnt require re-training a DCNN. Our method combines image quality assessment with knowledge distillation to detect input images that would trigger a DCCN to produce egregiously wrong results. Using the ResNet model trained on ImageNet as an example, we demonstrate that the detector can effectively identify poor quality and adversarial images.