The gaming industry has experienced substantial growth, but cheating in online games poses a significant threat to the integrity of the gaming experience. Cheating, particularly in first-person shooter (FPS) games, can lead to substantial losses for the game industry. Existing anti-cheat solutions have limitations, such as client-side hardware constraints, security risks, server-side unreliable methods, and both-sides suffer from a lack of comprehensive real-world datasets. To address these limitations, the paper proposes HAWK, a server-side FPS anti-cheat framework for the popular game CS:GO. HAWK utilizes machine learning techniques to mimic human experts’ identification process, leverages novel multi-view features, and is equipped with a well-defined workflow. HAWK is evaluated with the first large and real-world datasets containing multiple cheat types and cheating sophistication, and it exhibits promising efficiency and acceptable overheads, shorter ban times, higher recall and similar false positive rate compared to the in-use anti-cheat, and the ability to capture cheaters who evaded official inspections.

Hybrid testing, combining fuzz testing and concolic execution, has emerged as an effective technique for bug discovery. However, concolic execution becomes the performance bottleneck when applied to real-world software. Despite numerous approaches to optimize seed scheduling, symbolic simulation, and constraint solving, concolic execution remains inefficient and ineffective due to two limitations. First, the concolic executor and fuzzer do not synchronize the testing state in real time, leading to the generation of numerous duplicate inputs in both concolic execution and the fuzzer. Second, the concolic executor overlooks the independence of constraint solving and solves constraints sequentially, which introduces significant slowdown. In this paper, we first conduct a study to identify these limitations in existing hybrid testing systems. We then propose a novel design for hybrid fuzzing,CherryPicker, where the fuzzer and concolic executor share testing states, and concolic execution runs in parallel mode. Finally, we evaluate our system using the LAVA-M benchmark and real-world software and compare it to state-of-the-art systems. The results demonstrate thatCherryPickeroutperforms current systems in terms of efficiency and effectiveness, delivering improved runtime performance, generating more intriguing inputs, and activating more code. Notably,CherryPickerexclusively uncovers six previously unknown bugs during the evaluation, which have been reported to developers, all of which have been confirmed with three CVEs assigned.

Person re-identification (re-id) is a promising topic in computer vision, which concentrates on similarity learning of individuals across different camera views. It remains challenging due to the unpredictable orientation variations, the partial occlusions, and the inaccurate detections. To solve these problems, we present an orientation-guided similarity learning architecture to learn discriminative feature representations and define similarity metric for person re-id. Our proposed architecture explicitly leverages pedestrian orientation and body part cues to enhance the generalization ability. In the architecture, an orientation-guided loss function that pulls the positive samples with the same orientations closer is designed to alleviate the orientation variations. Meanwhile, an aligned dense network with pose estimation is presented to extract robust global-local fusion representations, which effectively exploits local features to overcome partial occlusions. In the end, we introduce a two-stage Top-k reranking strategy to optimize initial re-id results by min-hash and weighted distance. Extensive experimental results demonstrate that our proposed approach significantly outperforms state-of-the-art re-id methods on the popular CUHK03, Market1501, and DukeMTMC-reID datasets.