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An Information-Theoretic Explanation for the Adversarial Fragility of AI Classifiers
We present a simple hypothesis about a compression property of artificial intelligence (AI) classifiers and present theoretical arguments to show that this hypothesis successfully accounts for the observed fragility of AI classifiers to small adversarial perturbations.
Do Deep Minds Think Alike? Selective Adversarial Attacks for Fine-Grained Manipulation of Multiple Deep Neural Networks
Recent works have demonstrated the existence of {\it adversarial examples} targeting a single machine learning system.
Derivation of Information-Theoretically Optimal Adversarial Attacks with Applications to Robust Machine Learning
We consider the theoretical problem of designing an optimal adversarial attack on a decision system that maximally degrades the achievable performance of the system as measured by the mutual information between the degraded signal and the label of interest.
Optimal Pooling Matrix Design for Group Testing with Dilution (Row Degree) Constraints
In this paper, we consider the problem of designing optimal pooling matrix for group testing (for example, for COVID-19 virus testing) with the constraint that no more than $r>0$ samples can be pooled together, which we call "dilution constraint".
Linear Progressive Coding for Semantic Communication using Deep Neural Networks
We propose a general method for semantic representation of images and other data using progressive coding.
