BACKGROUND: Orthologous gene inference is a crucial technical challenge in evolutionary biology. It typically depends on sequence similarity searches and employs a graph clustering method to infer homologous gene families. However, the all-vs-all sequence similarity search is time-consuming for large-scale genome datasets. In this work, we present LSGFA, a method that detects subgraphs based on the similarity of protein domain architectures and then performs graph clustering within each subgraph, corresponding to sequences that share similar compositions of protein domains. RESULTS: LSGFA carries out four steps in the analysis workflow: protein domain annotation, initial clustering based on Pfam domain architecture, SSN-based clustering, and detection of pan-genomic patterns. Benchmarking against five state-of-the-art tools (OrthoFinder, Roary, PanTA, Panaroo, and PGAP2) across multiple datasets demonstrates that LSGFA achieves a balanced trade-off between computational efficiency and biological accuracy. It takes less time than OrthoFinder while identifying more core genes than high-speed heuristic tools, and its orthogroup inference results show strong consistency with OrthoFinder. CONCLUSIONS: Due to the high proportion of proteins with known domain architectures in prokaryotes, LSGFA is particularly well-suited for prokaryotic genomes, where it significantly reduces computational time while yielding accurate homologous gene inference.

Voice synthesis technologies have advanced rapidly, raising serious concerns about content security and trust. While many fake voice detectors achieve strong performance in controlled settings, they often overfit to speaker-irrelative features (SiFs), exhibit poor robustness, and fail in multi-speaker scenarios. To address these limitations, we propose MultiSiFer, a novel fake voice detector grounded in a new design philosophy: rather than merely distinguishing synthetic from human voices, it explicitly prioritizes learning essential human voice characteristics. MultiSiFer leverages a pre-trained speech representation model to enhance this learning and is the first detector trained on a newly curated multi-speaker fake voice dataset, enabling effective generalization across speakers. Experiments show that MultiSiFer outperforms existing methods in both standard and multi-speaker settings, achieving 10.84% average equal error rate (EER).

The application of deep learning in voice cloning has significantly enhanced the quality of cloned voices. While advanced voice cloning technologies are widely applied across various domains, they also pose serious security challenges such as producing natural Deepfakes. In response, numerous studies have focused on detecting fake voices, with many reporting outstanding performance. However, is the issue truly resolved? This paper introduces Adversarial Neural Mimicry Attack (ANMA) which leverages a specialized model to predict the behavior of other similar models, transforming black-box attacks into white-box scenarios indirectly. Based on ANMA and Speaker-irrelative Features (SiFs), we propose a novel black-box attack framework called SiFMimicEvader, designed to evade fake voice detectors with high success rates and minimal query requirements. The framework utilizes speech representation models as the breakthrough to predict the behaviors of fake voice detectors and employs a series of SiFs editing operations as perturbations to deceive these detectors. Experimental results demonstrate the effectiveness of SiFMimicEvader, achieving an average attack success rate exceeding 50% across various detectors, significantly outperforming other attack methods, while also showing great performance in audio quality and query scale, indicating its high availability in real-world scenarios.

Recent speech synthesis technology can generate high-quality speech indistinguishable from human speech, thus introducing various security and privacy risks. Numerous recent studies have focused on fake voice detection to address these risks, with many claiming to achieve ideal performance. However, is this really the case? A recent research work introduced Speaker-Irrelative-Features (SiFs), unrelated to the information in speech files but capable of influencing fake detectors. This means that existing detectors may rely on SiFs to a certain extent to distinguish real and fake speech. In this paper, we introduce an evaluation framework to evaluate the influence of SiFs in existing fake voice detectors in depth. We evaluate three SiFs which include background noise, the mute parts before and after voice, and the sampling rate on ASVspoof2019 and FoR. Our results confirm the substantial influence of SiFs on fake voice detection performance, and we delve into the analysis of the underlying mechanisms.

Voice is one of the most widely used media for information transmission in human society. While high-quality synthetic voices are extensively utilized in various applications, they pose significant risks to content security and trust building. Numerous studies have concentrated on AI-synthesized voice detection to mitigate these risks, with many claiming to achieve promising performance. However, recent research has demonstrated that fake voice detectors suffer from serious overfitting to speaker-irrelative features (SiFs) and cannot be used in real-world scenarios. In this paper, we analyze the limitations of existing fake voice detectors and propose a new design philosophy, guiding the detection model to prioritize learning human voice features rather than the difference between the human voice and the synthetic voice. Based on this philosophy, we propose a novel AI-synthesized voice detection framework named SiFSafer, which uses pre-trained speech representation models to enhance the learning of feature distribution in human voices and the adapter fine-tuning to optimize the performance. The evaluation shows that the average EERs of existing fake voice detectors in the ASVspoof datasets can exceed 20% if the SiFs like silence segments are removed, while SiFSafer achieves an EER of less than 8%, indicating that SiFSafer is robust to SiFs and strongly resistant to existing attacks.

Voice is an essential medium for human communication and collaboration, and its trustworthiness is of great importance to humans. Synthesizing fake voices and detecting synthesized voices are two sides of a coin. Both sides have made great strides with the recently prospering deep learning techniques. Attackers started using AI techniques to synthesize, even clone, human voices. Researchers also proposed a series of AI-synthesized voice detection approaches and achieved promising results in laboratory environments.In this paper, we introduced the concept of speaker-irrelative features (SiFs) and a novel detection-bypass idea to camouflage AI-synthesized voices: replacing SiFs of AI-synthesized voices with crafted ones. We implemented a proof-of-concept framework named SiF-DeepVC based on our detection-bypass idea. Experiments show that the existing detection systems would consider the voices output by SiF-DeepVC more human-like than human voices, proving our detection-bypass idea is effective and SiFs are noteworthy in camouflaging AI-synthesized voices.

Voice is a vital medium for transmitting information. The advancement of speech synthesis technology has resulted in high-quality synthesized voices indistinguishable from human ears. These fake voices have been widely used in natural Deepfake production and other malicious activities, raising serious concerns regarding security and privacy. To deal with this situation, there have been many studies working on detecting fake voices and reporting excellent performance. However, is the story really over? In this paper, we propose SiFDetectCracker, a black-box adversarial attack framework based on Speaker-Irrelative Features (SiFs) against fake voice detection. We select background noise and mute parts before and after the speaker's voice as the primary attack features. By modifying these features in synthesized speech, the fake speech detector will make a misjudgment. Experiments show that SiFDetectCracker achieved a success rate of more than 80% in bypassing existing state-of-the-art fake voice detection systems. We also conducted several experiments to evaluate our attack approach's transferability and activation factor.