Abstract


Counterfactual explanations and adversarial attacks have a related goal: flipping output labels with minimal perturbations regardless of their characteristics. Yet, adversarial attacks cannot be used directly in a counterfactual explanation perspective, as such perturbations are perceived as noise and not as actionable and understandable image modifications. Building on the robust learning literature, this paper proposes an elegant method to turn adversarial attacks into semantically meaningful perturbations, without modifying the classifiers to explain. The proposed approach hypothesizes that Denoising Diffusion Probabilistic Models are excellent regularizers for avoiding high-frequency and out-of-distribution perturbations when generating adversarial attacks. The paper's key idea is to build attacks through a diffusion model to polish them. This allows studying the target model regardless of its robustification level. Extensive experimentation shows the advantages of our counterfactual explanation approach over current State-of-the-Art in multiple testbeds.

Pipeline


This is a picture of our project ACE

Attack Evolution


Left: Pre-Explanation | Right: Filtered Explanation

With ACE, we can create natural adversarial examples


ACE helps in finding weaknesses.

ACE detects biases

ACE vs DiME


ACE qualitatively performs better than previous State-of-the-Art.

ACE vs DiME

Citation


                    

@inproceedings{Jeanneret_2023_CVPR, author = {Jeanneret, Guillaume and Simon, Lo\"ic and Fr\'ed\'eric Jurie}, title = {Adversarial Counterfactual Visual Explanations}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2023} }