Perceptual speech hash and robust watermarking have been widely investigated to solve the problems of authenticating speech integrity. The former generates a watermark and the latter embeds the watermark into the speech signal to implement speech integrity authentication. In this paper, we propose a perceptual speech hash algorithm and a robust watermarking algorithm for speech integrity authentication. To obtain perceptual speech hash values, we propose a gammatone filter model of the speech signal to extract sensitive auditory features (denoted by gammatone features). A random Gaussian matrix is used to reduce the dimensionality of the features of the gammatone to generate perceptual speech hash values. For the watermarking algorithm, we construct learned dictionaries to obtain the robust sparse feature of coefficients of the stationary wavelet transforms, and embed a watermark (perceptual speech hash values) into the sparse feature by patchwork and quantization index modulation. We illustrate the good imperceptibility of the authentication scheme in terms of the signal-to-noise ratio, objective difference grade, and subjective difference grade, and verify its robustness against common signal processing operations while maintaining imperceptibility. Moreover, our proposed method is sensitive to the malicious modification of the watermarked speech. Compared with state-of-the-art algorithms, the proposed algorithm can obtain better comprehensive performance in the detection and localization of tampering with the content of speech.

The integration of convolutional neural network (CNN) and transformer enhances the network’s capacity for concurrent modeling of texture details and global structures. However, training challenges with transformer limit their effectiveness to low-resolution images, leading to increased artifacts in slightly larger images. In this paper, we propose a single-stage network utilizing large kernel attention (LKA) to address high-resolution damaged images. LKA enables the capture of both global and local details, akin to transformer and CNN networks, resulting in high-quality inpainting. Our method excels in: (1) reducing parameters, improving inference speed, and enabling direct training on 1024 $$\times $$× 1024 resolution images; (2) utilizing LKA for enhanced extraction of global high-frequency and local details; (3) demonstrating excellent generalization on irregular mask models and common datasets such as Places2, Celeba-HQ, FFHQ, and the random irregular mask dataset Pconv from NVIDIA.