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Found 4 papers, 0 papers with code
Efficient Model Stealing Defense with Noise Transition Matrix
With the escalating complexity and investment cost of training deep neural networks safeguarding them from unauthorized usage and intellectual property theft has become imperative.
One Model for All: Large Language Models are Domain-Agnostic Recommendation Systems
We expect that by mixing the user's behaviors across different domains, we can exploit the common knowledge encoded in the pre-trained language model to alleviate the problems of data sparsity and cold start problems.
Improving Transferability of Adversarial Patches on Face Recognition with Generative Models
However, deep CNNs are vulnerable to adversarial patches, which are physically realizable and stealthy, raising new security concerns on the real-world applications of these models.
Data-Free Adversarial Perturbations for Practical Black-Box Attack
Adversarial examples often exhibit black-box attacking transferability, which allows that adversarial examples crafted for one model can fool another model.
