Mar 17, 2023 · This paper proposes an elegant method to turn adversarial attacks into semantically meaningful perturbations, without modifying the classifiers to explain.
Counterfactual explanations and adversarial attacks have a related goal: flipping output labels with minimal perturbations regardless of their characteristics.
This repository contains the official code for the CVPR 2023 paper Adversarial Counterfactual Visual Explanations.
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This paper proposes an elegant method to turn adversarial attacks into semantically meaningful perturbations, without modifying the classifiers to explain.
Counterfactual explanations and adversarial attacks have a related goal: flipping output labels with minimal perturbations regardless of their characteristics.
Mar 17, 2023 · Building on the robust learning literature, this paper proposes an elegant method to turn adversarial attacks into semantically meaningful ...
The key idea is to build attacks through a diffusion model to polish them, which allows studying the target model regardless of its robustification level, ...
The final result is the counterfactual explanation. Algorithm 1 Pre-explanation generation. Require: Diffusion Model D, Distance loss d and its reg- ularization ...
This paper addresses the challenge of generating Counterfactual Explanations (CEs), involving the identification and modification of the fewest necessary ...
Counterfactual explanations and adversarial attacks have a related goal: flipping output labels with minimal perturbations regardless of their characteristics.