While efforts have been made to explain computer vision models, these approaches often fall short of providing direct and human-understandable explanations.

Although numerous studies have been emerged on adversarial attacks and defenses in fields such as face recognition, person re-identification, and pedestrian detection, there is currently a lack of research on the security of VIReID systems.

Membership Inference Attacks (MIA) aim to infer whether a target data record has been utilized for model training or not.

We introduce the Continuous Arcade Learning Environment (CALE), an extension of the well-known Arcade Learning Environment (ALE) [Bellemare et al., 2013].

The traditional approach of pixel-level alignment is ineffective for diffusion-processed frames because of iterative disruptions.

Differential privacy (DP) is a widely used approach for mitigating privacy risks when training machine learning models on sensitive data. DP mechanisms add noise during training to limit the risk of information leakage. The scale of the added noise is critical, as it determines the trade-off between privacy and utility.

The development of deep learning approaches for modeling these multifactorial effects of GVs is still in its nascent stages, primarily due to the lack of comprehensive datasets that capture the intricate relationships between GVs and their downstream effects on complex traits.