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09/11/2023
Hazards in Deep Learning Testing: Prevalence, Impact and Recommendations
Much research on Machine Learning testing relies on empirical studies th...
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05/24/2023
How do humans perceive adversarial text? A reality check on the validity and naturalness of word-based adversarial attacks
Natural Language Processing (NLP) models based on Machine Learning (ML) ...
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02/06/2023
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks
While leveraging additional training data is well established to improve...
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12/15/2022
On Evaluating Adversarial Robustness of Chest X-ray Classification: Pitfalls and Best Practices
Vulnerability to adversarial attacks is a well-known weakness of Deep Ne...
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02/07/2022
On The Empirical Effectiveness of Unrealistic Adversarial Hardening Against Realistic Adversarial Attacks
While the literature on security attacks and defense of Machine Learning...
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12/02/2021
A Unified Framework for Adversarial Attack and Defense in Constrained Feature Space
The generation of feasible adversarial examples is necessary for properl...
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10/26/2021
Adversarial Robustness in Multi-Task Learning: Promises and Illusions
Vulnerability to adversarial attacks is a well-known weakness of Deep Ne...
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06/12/2020
Data-driven Simulation and Optimization for Covid-19 Exit Strategies
The rapid spread of the Coronavirus SARS-2 is a major challenge that led...
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11/14/2019
Adversarial Embedding: A robust and elusive Steganography and Watermarking technique
We propose adversarial embedding, a new steganography and watermarking t...
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04/09/2019
