1 citations · 1 across the 3 of their papers we have counts for
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Differentially Private and Adversarially Robust Machine Learning: An Empirical Evaluation
Janvi Thakkar, Giulio Zizzo, Sergio Maffeis
Malicious adversaries can attack machine learning models to infer sensitive information or damage the system by launching a series of evasion attacks. Although various work address…
Elevating Defenses: Bridging Adversarial Training and Watermarking for Model Resilience
Janvi Thakkar, Giulio Zizzo, Sergio Maffeis
Machine learning models are being used in an increasing number of critical applications; thus, securing their integrity and ownership is critical. Recent studies observed that adve…
Merged-GHCIDR: Geometrical Approach to Reduce Image Data
Devvrat Joshi, Janvi Thakkar, Siddharth Soni +3
The computational resources required to train a model have been increasing since the inception of deep networks. Training neural networks on massive datasets have become a challeng…