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Found 13 papers, 8 papers with code
On the (In)feasibility of ML Backdoor Detection as an Hypothesis Testing Problem
We introduce a formal statistical definition for the problem of backdoor detection in machine learning systems and use it to analyze the feasibility of such problems, providing evidence for the utility and applicability of our definition.
Optimal Zero-Shot Detector for Multi-Armed Attacks
We approach this defensive strategy with utmost caution, operating in an environment where the defender possesses significantly less information compared to the attacker.
Disparate Impact on Group Accuracy of Linearization for Private Inference
Ensuring privacy-preserving inference on cryptographically secure data is a well-known computational challenge.
Retrieval-Guided Reinforcement Learning for Boolean Circuit Minimization
Logic synthesis, a pivotal stage in chip design, entails optimizing chip specifications encoded in hardware description languages like Verilog into highly efficient implementations using Boolean logic gates.
A Data-Driven Measure of Relative Uncertainty for Misclassification Detection
Misclassification detection is an important problem in machine learning, as it allows for the identification of instances where the model's predictions are unreliable.
INVICTUS: Optimizing Boolean Logic Circuit Synthesis via Synergistic Learning and Search
%Compared to prior work, INVICTUS is the first solution that uses a mix of RL and search methods joint with an online out-of-distribution detector to generate synthesis recipes over a wide range of benchmarks.
A Minimax Approach Against Multi-Armed Adversarial Attacks Detection
Multi-armed adversarial attacks, in which multiple algorithms and objective loss functions are simultaneously used at evaluation time, have been shown to be highly successful in fooling state-of-the-art adversarial examples detectors while requiring no specific side information about the detection mechanism.
MEAD: A Multi-Armed Approach for Evaluation of Adversarial Examples Detectors
Detection of adversarial examples has been a hot topic in the last years due to its importance for safely deploying machine learning algorithms in critical applications.
Perfectly Accurate Membership Inference by a Dishonest Central Server in Federated Learning
Federated Learning is expected to provide strong privacy guarantees, as only gradients or model parameters but no plain text training data is ever exchanged either between the clients or between the clients and the central server.
DOCTOR: A Simple Method for Detecting Misclassification Errors
Deep neural networks (DNNs) have shown to perform very well on large scale object recognition problems and lead to widespread use for real-world applications, including situations where DNN are implemented as "black boxes".
Estimating g-Leakage via Machine Learning
A feature of our approach is that it does not require to estimate the conditional probabilities, and that it is suitable for a large class of ML algorithms.
Feature selection in machine learning: Rényi min-entropy vs Shannon entropy
Many algorithms for feature selection in the literature have adopted the Shannon-entropy-based mutual information.
Optimal Obfuscation Mechanisms via Machine Learning
The idea is to set up two nets: the generator, that tries to produce an optimal obfuscation mechanism to protect the data, and the classifier, that tries to de-obfuscate the data.
