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Found 8 papers, 7 papers with code
Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models
We show that Shadowcast are highly effective in achieving attacker's intentions using as few as 50 poison samples.
Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences
However, current MLLM benchmarks are predominantly designed to evaluate reasoning based on static information about a single image, and the ability of modern MLLMs to extrapolate from image sequences, which is essential for understanding our ever-changing world, has been less investigated.
Benchmarking the Robustness of Image Watermarks
We present WAVES (Watermark Analysis Via Enhanced Stress-testing), a novel benchmark for assessing watermark robustness, overcoming the limitations of current evaluation methods. WAVES integrates detection and identification tasks, and establishes a standardized evaluation protocol comprised of a diverse range of stress tests.
C-Disentanglement: Discovering Causally-Independent Generative Factors under an Inductive Bias of Confounder
Representation learning assumes that real-world data is generated by a few semantically meaningful generative factors (i. e., sources of variation) and aims to discover them in the latent space.
Equal Long-term Benefit Rate: Adapting Static Fairness Notions to Sequential Decision Making
Moreover, we show that the policy gradient of Long-term Benefit Rate can be analytically reduced to standard policy gradient.
Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness
However, it is unclear whether existing robust training methods effectively increase the margin for each vulnerable point during training.
Multi-Task Adversarial Attack
More specifically, MTA uses a generator for adversarial perturbations which consists of a shared encoder for all tasks and multiple task-specific decoders.
FREEtree: A Tree-based Approach for High Dimensional Longitudinal Data With Correlated Features
This paper proposes FREEtree, a tree-based method for high dimensional longitudinal data with correlated features.
