Adversarial examples by perturbing high-level features in intermediate decoder layers
This work addresses the challenge of generating more robust and semantically meaningful adversarial attacks for computer vision systems, which is incremental as it builds on existing adversarial example methods by focusing on feature-level perturbations.
The paper tackles the problem of creating adversarial examples by perturbing high-level features in intermediate decoder layers instead of pixels, resulting in semantically meaningful changes like longer beaks or green tints, and demonstrates that these adversarial images are less vulnerable to steganographic defenses and can bypass adversarial training defenses on MNIST and ImageNet datasets.
We propose a novel method for creating adversarial examples. Instead of perturbing pixels, we use an encoder-decoder representation of the input image and perturb intermediate layers in the decoder. This changes the high-level features provided by the generative model. Therefore, our perturbation possesses semantic meaning, such as a longer beak or green tints. We formulate this task as an optimization problem by minimizing the Wasserstein distance between the adversarial and initial images under a misclassification constraint. We employ the projected gradient method with a simple inexact projection. Due to the projection, all iterations are feasible, and our method always generates adversarial images. We perform numerical experiments on the MNIST and ImageNet datasets in both targeted and untargeted settings. We demonstrate that our adversarial images are much less vulnerable to steganographic defence techniques than pixel-based attacks. Moreover, we show that our method modifies key features such as edges and that defence techniques based on adversarial training are vulnerable to our attacks.