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Found 6 papers, 1 papers with code
MergePrint: Robust Fingerprinting against Merging Large Language Models
As the cost of training large language models (LLMs) rises, protecting their intellectual property has become increasingly critical.
Leveraging Many-To-Many Relationships for Defending Against Visual-Language Adversarial Attacks
To this end, this paper studies defense strategies against adversarial attacks on VL models for ITR for the first time.
Rethinking Invariance Regularization in Adversarial Training to Improve Robustness-Accuracy Trade-off
Although adversarial training has been the state-of-the-art approach to defend against adversarial examples (AEs), it suffers from a robustness-accuracy trade-off, where high robustness is achieved at the cost of clean accuracy.
Defending Against Physical Adversarial Patch Attacks on Infrared Human Detection
To address this problem, we are the first to investigate defense strategies against adversarial patch attacks on infrared detection, especially human detection.
Beyond In-Domain Scenarios: Robust Density-Aware Calibration
While existing post-hoc calibration methods achieve impressive results on in-domain test datasets, they are limited by their inability to yield reliable uncertainty estimates in domain-shift and out-of-domain (OOD) scenarios.
Closer Look at the Transferability of Adversarial Examples: How They Fool Different Models Differently
Deep neural networks are vulnerable to adversarial examples (AEs), which have adversarial transferability: AEs generated for the source model can mislead another (target) model's predictions.
