Timbre-reserved Adversarial Attack in Speaker Identification

As a type of biometric identification, a speaker identification (SID) system is confronted with various kinds of attacks. The spoofing attacks typically imitate the timbre of the target speakers, while the adversarial attacks confuse the SID system by adding a well-designed adversarial perturbation to an arbitrary speech. Although the spoofing attack copies a similar timbre as the victim, it does not exploit the vulnerability of the SID model and may not make the SID system give the attacker's desired decision. As for the adversarial attack, despite the SID system can be led to a designated decision, it cannot meet the specified text or speaker timbre requirements for the specific attack scenarios. In this study, to make the attack in SID not only leverage the vulnerability of the SID model but also reserve the timbre of the target speaker, we propose a timbre-reserved adversarial attack in the speaker identification. We generate the timbre-reserved adversarial audios by adding an adversarial constraint during the different training stages of the voice conversion (VC) model. Specifically, the adversarial constraint is using the target speaker label to optimize the adversarial perturbation added to the VC model representations and is implemented by a speaker classifier joining in the VC model training. The adversarial constraint can help to control the VC model to generate the speaker-wised audio. Eventually, the inference of the VC model is the ideal adversarial fake audio, which is timbre-reserved and can fool the SID system.Comment: 11 pages, 8 figure

Arbuscular Mycorrhizal Fungi Improve the Antioxidative Response and the Seed Production of Suaedoideae Species Suaeda physophora Pall Under Salt Stress

Arbuscular mycorrhizal fungi (AMF) play a key role in plant growth and survival; however, the influence of AMF on the growth and production of Suaedoideae species is still not well understood. The object of this study was to understand the mechanism of AMF that affects the growth of Suaedoideae species under different saline conditions. The result showed that the Suaedoideae species Suaeda physophora was colonized by the AMF species Glomus etunicatum (Ge) and Glomus mosseae (Gm). AMF significantly increased the activities of superoxide dismutase (SOD) and peroxidase (POD) in S. physophora and reduced the concentrations of malondialdehyde (MDA) and H2O2 in the leaves of S. physophora under salt stress. AMF also improved the aboveground biomass of S. physophora and significantly increased its seed numbers. Moreover, AMF increased the aboveground phosphorus (P) content of S. physophora. No significant difference between the effect of AMF species Ge and Gm on S. physophora growth was observed. These results suggest that AMF can increase the salt resistance of the Suaedoideae species S. physophora by increasing SOD and POD activities, reducing MDA and H2O2 concentrations and increasing P uptake. The results highlight that AMF might play an important role in S. physophora growth and population survival under harsh salt conditions

Pseudo-Siamese Network based Timbre-reserved Black-box Adversarial Attack in Speaker Identification

In this study, we propose a timbre-reserved adversarial attack approach for speaker identification (SID) to not only exploit the weakness of the SID model but also preserve the timbre of the target speaker in a black-box attack setting. Particularly, we generate timbre-reserved fake audio by adding an adversarial constraint during the training of the voice conversion model. Then, we leverage a pseudo-Siamese network architecture to learn from the black-box SID model constraining both intrinsic similarity and structural similarity simultaneously. The intrinsic similarity loss is to learn an intrinsic invariance, while the structural similarity loss is to ensure that the substitute SID model shares a similar decision boundary to the fixed black-box SID model. The substitute model can be used as a proxy to generate timbre-reserved fake audio for attacking. Experimental results on the Audio Deepfake Detection (ADD) challenge dataset indicate that the attack success rate of our proposed approach yields up to 60.58% and 55.38% in the white-box and black-box scenarios, respectively, and can deceive both human beings and machines.Comment: 5 page

Distinguishable Speaker Anonymization based on Formant and Fundamental Frequency Scaling

Speech data on the Internet are proliferating exponentially because of the emergence of social media, and the sharing of such personal data raises obvious security and privacy concerns. One solution to mitigate these concerns involves concealing speaker identities before sharing speech data, also referred to as speaker anonymization. In our previous work, we have developed an automatic speaker verification (ASV)-model-free anonymization framework to protect speaker privacy while preserving speech intelligibility. Although the framework ranked first place in VoicePrivacy 2022 challenge, the anonymization was imperfect, since the speaker distinguishability of the anonymized speech was deteriorated. To address this issue, in this paper, we directly model the formant distribution and fundamental frequency (F0) to represent speaker identity and anonymize the source speech by the uniformly scaling formant and F0. By directly scaling the formant and F0, the speaker distinguishability degradation of the anonymized speech caused by the introduction of other speakers is prevented. The experimental results demonstrate that our proposed framework can improve the speaker distinguishability and significantly outperforms our previous framework in voice distinctiveness. Furthermore, our proposed method also can trade off the privacy-utility by using different scaling factors.Comment: Submitted to ICASSP 202

Preserving background sound in noise-robust voice conversion via multi-task learning

Background sound is an informative form of art that is helpful in providing a more immersive experience in real-application voice conversion (VC) scenarios. However, prior research about VC, mainly focusing on clean voices, pay rare attention to VC with background sound. The critical problem for preserving background sound in VC is inevitable speech distortion by the neural separation model and the cascade mismatch between the source separation model and the VC model. In this paper, we propose an end-to-end framework via multi-task learning which sequentially cascades a source separation (SS) module, a bottleneck feature extraction module and a VC module. Specifically, the source separation task explicitly considers critical phase information and confines the distortion caused by the imperfect separation process. The source separation task, the typical VC task and the unified task shares a uniform reconstruction loss constrained by joint training to reduce the mismatch between the SS and VC modules. Experimental results demonstrate that our proposed framework significantly outperforms the baseline systems while achieving comparable quality and speaker similarity to the VC models trained with clean data.Comment: Submitted to ICASSP 202

Warming and Nitrogen Addition Alter Photosynthetic Pigments, Sugars and Nutrients in a Temperate Meadow Ecosystem.

Global warming and nitrogen (N) deposition have an important influence on terrestrial ecosystems; however, the influence of warming and N deposition on plant photosynthetic products and nutrient cycling in plants is not well understood. We examined the effects of 3 years of warming and N addition on the plant photosynthetic products, foliar chemistry and stoichiometric ratios of two dominant species, i.e., Leymus chinensis and Phragmites communis, in a temperate meadow in northeastern China. Warming significantly increased the chlorophyll content and soluble sugars in L. chinensis but had no impact on the carotenoid and fructose contents. N addition caused a significant increase in the carotenoid and fructose contents. Warming and N addition had little impact on the photosynthetic products of P. communis. Warming caused significant decreases in the N and phosphorus (P) concentrations and significantly increased the carbon (C):P and N:P ratios of L. chinensis, but not the C concentration or the C:N ratio. N addition significantly increased the N concentration, C:P and N:P ratios, but significantly reduced the C:N ratio of L. chinensis. Warming significantly increased P. communis C and P concentrations, and the C:N and C:P ratios, whereas N addition increased the C, N and P concentrations but had no impact on the stoichiometric variables. This study suggests that both warming and N addition have direct impacts on plant photosynthates and elemental stoichiometry, which may play a vital role in plant-mediated biogeochemical cycling in temperate meadow ecosystems