Qi Xin

Yuankun Xie, Yi Lu, Ruibo Fu et al.

With the proliferation of Audio Language Model (ALM) based deepfake audio, there is an urgent need for generalized detection methods. ALM-based deepfake audio currently exhibits widespread, high deception, and type versatility, posing a significant challenge to current audio deepfake detection (ADD) models trained solely on vocoded data. To effectively detect ALM-based deepfake audio, we focus on the mechanism of the ALM-based audio generation method, the conversion from neural codec to waveform. We initially constructed the Codecfake dataset, an open-source, large-scale collection comprising over 1 million audio samples in both English and Chinese, focus on ALM-based audio detection. As countermeasure, to achieve universal detection of deepfake audio and tackle domain ascent bias issue of original sharpness aware minimization (SAM), we propose the CSAM strategy to learn a domain balanced and generalized minima. In our experiments, we first demonstrate that ADD model training with the Codecfake dataset can effectively detects ALM-based audio. Furthermore, our proposed generalization countermeasure yields the lowest average equal error rate (EER) of 0.616% across all test conditions compared to baseline models. The dataset and associated code are available online.

Yi Lu, Yuankun Xie, Ruibo Fu et al.

With the proliferation of Large Language Model (LLM) based deepfake audio, there is an urgent need for effective detection methods. Previous deepfake audio generation methods typically involve a multi-step generation process, with the final step using a vocoder to predict the waveform from handcrafted features. However, LLM-based audio is directly generated from discrete neural codecs in an end-to-end generation process, skipping the final step of vocoder processing. This poses a significant challenge for current audio deepfake detection (ADD) models based on vocoder artifacts. To effectively detect LLM-based deepfake audio, we focus on the core of the generation process, the conversion from neural codec to waveform. We propose Codecfake dataset, which is generated by seven representative neural codec methods. Experiment results show that codec-trained ADD models exhibit a 41.406% reduction in average equal error rate compared to vocoder-trained ADD models on the Codecfake test set.

Steven P. Reiss, Qi Xin

Modern development environments provide a widely used auto-correction facility for quickly repairing syntactic errors. Auto-correction cannot deal with semantic errors, which are much more difficult to repair. Automated program repair techniques, designed for repairing semantic errors, are not well-suited for interactive use while debugging, as they typically assume the existence of a high-quality test suite and take considerable time. To bridge the gap, we developed ROSE, a tool to suggest quick-yet-effective repairs of semantic errors during debugging. ROSE does not rely on a test suite. Instead, it assumes a debugger stopping point where a problem is observed. It asks the developer to quickly describe what is wrong, performs a light-weight fault localization to identify potential responsible locations, and uses a generate-and-validate strategy to produce and validate repairs. Finally, it presents the results so the developer can choose and make the appropriate repair. To assess its utility, we implemented a prototype of ROSE that works in the Eclipse IDE and applied it to two benchmarks, QuixBugs and Defects4J, for repair. ROSE was able to suggest correct repairs for 17 QuixBugs and 16 Defects4J errors in seconds.