A significant challenge in audio deepfake detection (ADD) is to improve model generalization against unseen vocoders and other unknown factors, as existing methods often overfit to specific vocoder patterns or synthetic-irrelevant factors. To overcome this challenge, by focusing on vocoder-agnostic features and synthetic traces for generalizable ADD, we propose a novel dual-level disentanglement with meta-learning (ALDEN ) framework. Specifically, we first introduce an adversarial-training-based disentanglement learning (ADL) module to explicitly learn vocoder-agnostic and vocoder-specific features, effectively disentangling audio signals in terms of low-level characteristics. To suppress synthetic-irrelevant information, such as semantics and speaker identities, we simultaneously employ a reconstruction-based disentanglement learning (RDL) module, which further disentangles both synthetic-relevant and synthetic-irrelevant features from vocoder-agnostic features at a high-level of semantics. Additionally, as low-level non-semantic features are more critical in ADD, a vocoder-agnostic meta-learning (VAML) module is proposed to simulate cross-vocoder scenarios so as to further boost generalization performance. Extensive experiments demonstrate that ALDEN outperforms state-of-the-art methods in cross-vocoder and in-the-wild scenarios. The code, model, and supplementary materials will be released on the project page: https://beyond0814.github.io/ALDEN/.

In audio spoofing detection, most studies rely on clean datasets, making models susceptible to real-world post-processing attacks, such as channel compression and noise. To overcome this challenge, we propose the Adaptive MixtUre Low-rank ExperTs (AMULET) framework, which enhances resilience by leveraging attack-specific knowledge and dynamically adapting to varied attack conditions. Specifically, AMULET employs Attack-Specific Experts (ASEs) fine-tuned with Low-Rank Adaptation (LoRA), allowing each expert to focus on distinct post-processing patterns using just 1.13% of the parameters required for full fine-tuning. Furthermore, we introduce Adaptive Expert Fusion (AEF), which adaptively selects and integrates expert knowledge to enhance the robustness of spoofing detection. Experimental results demonstrate that AMULET significantly enhances robustness by improving noise resilience and exhibiting greater adaptability to unseen post-processing methods compared to models trained with full fine-tuning. Additionally, our framework outperforms both single expert and other expert aggregation strategies under various mixed attacks, demonstrating its superior robustness and adaptability in managing complex real-world scenarios. Both our data processing pipeline and model will be released athttps://github.com/foolishcqx/AMULET-AudioSpoof.

Digital watermarking is a widely adopted authentication technique and one of its primary concerns in practical usage is robustness. However, existing audio watermarking methods face challenges in countering desynchronization attacks and replay attacks, which can easily lead to watermark extraction failure. In this paper, we introduce a learning-based scheme, named DRAW (Dual-decoder-based Robust Audio Watermarking), to overcome the robustness issue. Specifically, a watermark encoder embeds payloads together with synch codes into audio frames with high imperceptibility. For reliable watermark extraction, two separate decoders are designed, one for Fixed Length Synchronization Decoding (FLSD) and the other for Variable Length Payload Decoding (VLPD). The dual decoders are trained with the encoder with different weights in the loss function by considering their different roles for watermark extraction. To better resist attacks, a distortion layer is incorporated in-between the encoder and the decoders to simulate distortion and facilitate end-to-end learning. For the more challenging replay attacks, a pre-trained Replay Attack Simulation Network (RASN) is applied to simplify the simulation of re-recording with background noise and reverberation. Extensive experimental results show that the proposed method can be applied to variable-length audio clips with better auditory quality, and it outperforms state-of-the-art methods in robustness against various kinds of attacks.

Hyperspectral image (HSI) clustering has been widely used in the field of remote sensing. However, most traditional clustering algorithms are not suitable for dealing with large-scale HSI due to their low clustering performance and high computational complexity. In this letter, we propose a novel fuzzy clustering algorithm, called fuzzy embedded clustering based on the bipartite graph (FECBG), to efficiently deal with large-scale HSI clustering problems. First, we propose the FECBG method that incorporates fuzzy clustering with the nonnegative regularization term based on the bipartite graph into a unified model, which has good clustering performance and reduces the sensitivity of fuzzy clustering to the initial cluster centers. Second, we adopt the fast spectral embedded method to obtain the low-dimensional representation of HSI data, to reduce the computational complexity. At last, we add the nonnegative regularization term based on the bipartite graph to fuzzy clustering, to constrain the solution space of the fuzzy membership matrix. Experimental results on several HSI data sets have demonstrated the efficiency and effectiveness of the proposed FECBG algorithm.