Delving into the pixels of adversarial samples
Despite extensive research into adversarial attacks, we do not know how adversarial attacks affect image pixels. Knowing how image pixels are affected by adversarial attacks has the potential to lead us to better adversarial defenses. Motivated by instances that we find where strong attacks do not transfer, we delve into adversarial examples at pixel level to scrutinize how adversarial attacks affect image pixel values. We consider several ImageNet architectures, InceptionV3, VGG19 and ResNet50, as well as several strong attacks. We find that attacks can have different effects at pixel level depending on classifier architecture. In particular, input pre-processing plays a previously overlooked role in the effect that attacks have on pixels. Based on the insights of pixel-level examination, we find new ways to detect some of the strongest current attacks.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Delving into adversarial attacks on deep policies
Adversarial examples have been shown to exist for a variety of deep learning architectures. Deep reinforcement learning has shown promising results on training agent policies directly on raw inputs such as image pixels. …
Deep Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Erosion Attack for Adversarial Training to Enhance Semantic Segmentation Robustness
Existing segmentation models exhibit significant vulnerability to adversarial attacks.To improve robustness, adversarial training incorporates adversarial examples into model training. However, existing attack methods co…
Semantic SegmentationDeep neural network loses attention to adversarial images
Adversarial algorithms have shown to be effective against neural networks for a variety of tasks. Some adversarial algorithms perturb all the pixels in the image minimally for the image classification task in image class…
image-classificationImage ClassificationDelving into Deep Image Prior for Adversarial Defense: A Novel Reconstruction-based Defense Framework
Deep learning based image classification models are shown vulnerable to adversarial attacks by injecting deliberately crafted noises to clean images. To defend against adversarial attacks in a training-free and attack-ag…
Adversarial Defenseimage-classificationImage ClassificationImage ReconstructionLearning with Noisy labels via Self-supervised Adversarial Noisy Masking
Collecting large-scale datasets is crucial for training deep models, annotating the data, however, inevitably yields noisy labels, which poses challenges to deep learning algorithms. Previous efforts tend to mitigate thi…
Image ClassificationLearning with noisy labels