As person image variations are likely to cause a part misalignment problem, most previous person Re-Identification (ReID) works may adopt local feature partition or additional landmark annotations to acquire aligned person features and boost ReID performance. However, such approaches either only achieve coarse-grained part alignments without considering detailed image variations within each part, or require extra annotated landmarks to train an available pose estimation model. In this work, we propose an effective Discriminable and Reliable Transformer (DRFormer) framework to learn part-aligned person representations with only person identity labels. Specifically, the DRFormer framework consists of Discriminable Feature Transformer (DFT) and Reliable Feature Transformer (RFT) modules, which generate discriminable and reliable high-order features, respectively. For reducing the dimension of high-order features, the DFT module utilizes a Self-Attentive Kronecker Product (SAKP) algorithm to promote the representational capabilities of compressed features via a self-attention strategy. For eliminating the background noise, the RFT module mines the foreground regions to adaptively aggregate foreground features via a Gumbel-Softmax strategy. Moreover, the proposed framework derives from an interpretable motivation and elegantly solves part misalignments without using feature partition or pose estimation. This paper theoretically and experimentally demonstrates the superiority of the proposed DRFormer framework, achieving state-of-the-art performance on various person ReID datasets.

The objective of a data-free closed-box adversarial attack is to attack a victim model without using internal information, training datasets or semantically similar substitute datasets. Concerned about stricter attack scenarios, recent studies have tried employing generative networks to synthesize data for training substitute models. Nevertheless, these approaches concurrently encounter challenges associated with unstable training and diminished attack efficiency. In this paper, we propose a novel query-efficient data-free closed-box adversarial attack method. To mitigate unstable training, for the first time, we directly manipulate the intermediate-layer feature of a generator without relying on any substitute models. Specifically, a label noise-based generation module is created to enhance the intra-class patterns by incorporating partial historical information during the learning process. Additionally, we present a feature-disturbed diversity generation method to augment the inter-class distance. Meanwhile, we propose an adaptive intra-class attack strategy to heighten attack capability within a limited query budget. In this strategy, entropy-based distance is utilized to characterize the relative information from model outputs, while positive classes and negative samples are used to enhance low attack efficiency. The comprehensive experiments conducted on six datasets demonstrate the superior performance of our method compared to six state-of-the-art data-free closed-box competitors in both label-only and probability-only attack scenarios. Intriguingly, our method can realize the highest attack success rate on the online Microsoft Azure model under an extremely low query budget. Additionally, the proposed approach not only achieves more stable training but also significantly reduces the query count for a more balanced data generation. Furthermore, our method can maintain the best performance under the existing defense models and a limited query budget.

Deep learning models achieve remarkable accuracy in computer vision tasks yet remain vulnerable to adversarial examples-carefully crafted perturbations to input images that can deceive these models into making confident but incorrect predictions. This vulnerability poses significant risks in high-stakes applications such as autonomous vehicles, security surveillance, and safety-critical inspection systems. While the existing literature extensively covers adversarial attacks in image classification, comprehensive analyses of such attacks on object detection systems remain limited. This article presents a novel taxonomic framework for categorizing adversarial attacks specific to object detection architectures, synthesizes existing robustness metrics, and provides a comprehensive empirical evaluation of state-of-the-art attack methodologies on popular object detection models, including both traditional detectors and modern detectors with vision-language pretraining. Through rigorous analysis of open-source attack implementations and their effectiveness across diverse detection architectures, we derive key insights into attack characteristics. Furthermore, we delineate critical research gaps and emerging challenges to guide future investigations in securing object detection systems against adversarial threats. Our findings establish a foundation for developing more robust detection models while highlighting the urgent need for standardized evaluation protocols in this rapidly evolving domain.