… deep models in terms of salient visual features for class labels and highlighting the alignment
of deep representation with human perception, our attack … the first attack on deep learning …

… 5.2 Explanation Experiments Preliminary results of model interpretation through our attack
to explain deep representation were presented in CVPR2020 [18]. Setup: Our setup for these …

… risks for privacy attacks. Hence, providing explanation harms privacy. We study this risk
for image-based model inversion attacks and identified several attack architectures with …

… In these layers, we assess the impact of adversarial attacks on the internal representations
… To assess this, we evaluate the cosine similarities between attacked and non-attacked …

… layer to construct a general and localizable deep representation. … of representation-guided
reveals more privacy. In view of different attack datasets, we analyze that even if the attack …

… In these layers, we assess the impact of adversarial attacks on the internal representations
… To assess this, we evaluate the cosine similarities between attacked and nonattacked …

… representation of machine learning methods. Based on these metrics, we reveal a link between
deep representations’ quality and attack … two metrics to evaluate DNN’s representations. …

… • Experiment 3: To validate our proposed algorithm, we test the proposed attack model on
two representative pre-trained models, including feature module and no feature module. For …

… attack paradigms existing in this domain, identify current gaps and future research directions,
and illustrate the main attack … We will consider two representative explanation methods in …

… a deep model and its influence on model training is conditioned upon the presence of a trigger.
We design two types of attacks: a targeted attack … cases the hidden representations of the …