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Semantically Adversarial Learnable Filters
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AI
DOI：10.1109/TIP.2021.3112290.png)
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Abstract
En 中文
We present an adversarial framework to craft perturbations that mislead classifiers by accounting for the image content and the semantics of the labels. The proposed framework combines a structure loss and a semantic adversarial loss in a multi-task objective function to train a fully convolutional neural network. The structure loss helps generate perturbations whose type and magnitude are defined by a target image processing filter. The semantic adversarial loss considers groups of (semantic) labels to craft perturbations that prevent the filtered image from being classified with a label in the same group. We validate our framework with three different target filters, namely detail enhancement, log transformation and gamma correction filters; and evaluate the adversarially filtered images against three classifiers, ResNet50, ResNet18 and AlexNet, pre-trained on ImageNet. We show that the proposed framework generates filtered images with a high success rate, robustness, and transferability to unseen classifiers. We also discuss objective and subjective evaluations of the adversarial perturbations.
Keywords:
Perturbation methods
Semantics
Image color analysis
Image edge detection
Residual neural networks
Robustness
Nonlinear distortion
Adversarial examples
image filtering
image enhancement
Journal
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