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On the amplification of security and privacy risks by post-hoc explanations in machine learning models
A variety of explanation methods have been proposed in recent years to help users gain insights into the results returned by neural networks, which are otherwise complex and opaque black-boxes.
Towards Imperceptible Query-limited Adversarial Attacks with Perceptual Feature Fidelity Loss
Recently, there has been a large amount of work towards fooling deep-learning-based classifiers, particularly for images, via adversarial inputs that are visually similar to the benign examples.
Efficient Optimization Methods for Extreme Similarity Learning with Nonlinear Embeddings
We study the problem of learning similarity by using nonlinear embedding models (e. g., neural networks) from all possible pairs.
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