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Proof-of-Learning: Definitions and Practice
Training machine learning (ML) models typically involves expensive itera...
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Detecting Anomalous Inputs to DNN Classifiers By Joint Statistical Testing at the Layers
Detecting anomalous inputs, such as adversarial and out-of-distribution ...
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Face-Off: Adversarial Face Obfuscation
Advances in deep learning have made face recognition increasingly feasib...
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On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping
Machine learning algorithms are vulnerable to data poisoning attacks. Pr...
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Machine Unlearning
Once users have shared their data online, it is generally difficult for ...
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Generating Semantic Adversarial Examples with Differentiable Rendering
Machine learning (ML) algorithms, especially deep neural networks, have ...
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Enhancing ML Robustness Using Physical-World Constraints
Recent advances in Machine Learning (ML) have demonstrated that neural n...
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Characterizing Privacy Perceptions of Voice Assistants: A Technology Probe Study
The increasing pervasiveness of voice assistants in the home poses sever...
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Model Extraction and Active Learning
Machine learning is being increasingly used by individuals, research ins...
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