arXiv subjects

Yanwei Chen

Publications and source records attributed to Yanwei Chen.

3 recordsLinked to original sources

arXiv2025

DeCoMa: Detecting and Purifying Code Dataset Watermarks through Dual Channel Code Abstraction

Watermarking is a technique to help identify the source of data points, which can be used to help prevent the misuse of protected datasets. Existing methods on code watermarking, leveraging the idea from the backdoor research, embed stealthy triggers as watermarks. Despite their high resilience against dilution attacks and backdoor detections, the robustness has not been fully evaluated. To fill this gap, we propose DeCoMa, a dual-channel approach to Detect and purify Code dataset waterMarks. To overcome the high barrier created by the stealthy and hidden nature of code watermarks, DeCoMa leverages dual-channel constraints on code to generalize and map code samples into standardized templates. Subsequently, DeCoMa extracts hidden watermarks by identifying outlier associations between paired elements within the standardized templates. Finally, DeCoMa purifies the watermarked dataset by removing all samples containing the detected watermark, enabling the silent appropriation of protected code. We conduct extensive experiments to evaluate the effectiveness and efficiency of DeCoMa, covering 14 types of code watermarks and 3 representative intelligent code tasks (a total of 14 scenarios). Experimental results demonstrate that DeCoMa achieves a stable recall of 100% in 14 code watermark detection scenarios, significantly outperforming the baselines. Additionally, DeCoMa effectively attacks code watermarks with embedding rates as low as 0.1%, while maintaining comparable model performance after training on the purified dataset. Furthermore, as DeCoMa requires no model training for detection, it achieves substantially higher efficiency than all baselines, with a speedup ranging from 31.5 to 130.9X. The results call for more advanced watermarking techniques for code models, while DeCoMa can serve as a baseline for future evaluation. Code is available at https://github.com/xiaoyuanpigo/DeCoMa

cs.CR↗

arXiv2024

Pt nanoparticles dispersed in a metal-organic framework as peroxidase mimics for colorimetric detection of GSH

Metal-organic skeleton materials have been widely used in catalysis with their porous structure and adsorption properties. Precious metal nanoparticles have good catalytic properties. If the noble metal nanoparticles are adsorbed on the MOFs surface, the active sites can be increased and the catalytic effect of the materials can be greatly improved. We successfully synthesized Pt@ZIF-8 in two steps, the average particle size of Pt nanoparticles is about 3 nm. Pt@ZIF-8 possesses peroxidase activity and can oxidize colorless TMB to oxTMB in the presence of hydrogen peroxide. The peroxide-like nature of Pt@ZIF-8 is consistent with Michaelis-Menten kinetics. Glutathione is a reducing substance that reduces blue oxTMB to colorless oxTMB. This colorimetric method achieves a simple, sensitive and intuitive detection of glutathione. The detection limit of this experiment is low, which is promising in biomolecular detection.

physics.chem-ph↗

arXiv2022

Tightening Robustness Verification of MaxPool-based Neural Networks via Minimizing the Over-Approximation Zone

The robustness of neural network classifiers is important in the safety-critical domain and can be quantified by robustness verification. At present, efficient and scalable verification techniques are always sound but incomplete, and thus, the improvement of verified robustness results is the key criterion to evaluate the performance of incomplete verification approaches. The multi-variate function MaxPool is widely adopted yet challenging to verify. In this paper, we present Ti-Lin, a robustness verifier for MaxPool-based CNNs with Tight Linear Approximation. Following the sequel of minimizing the over-approximation zone of the non-linear function of CNNs, we are the first to propose the provably neuron-wise tightest linear bounds for the MaxPool function. By our proposed linear bounds, we can certify larger robustness results for CNNs. We evaluate the effectiveness of Ti-Lin on different verification frameworks with open-sourced benchmarks, including LeNet, PointNet, and networks trained on the MNIST, CIFAR-10, Tiny ImageNet and ModelNet40 datasets. Experimental results show that Ti-Lin significantly outperforms the state-of-the-art methods across all networks with up to 78.6% improvement in terms of the certified accuracy with almost the same time consumption as the fastest tool. Our code is available at https://github.com/xiaoyuanpigo/Ti-Lin-Hybrid-Lin.

cs.LG↗