Developing a unified model for surface anomaly detection remains challenging due to significant variations across product categories. Recent feature editing methods, as a branch of image reconstruction, mitigate the over-generalization of auto-encoders that leads to accurate anomaly reconstruction. However, these methods are only suited for texture-category products and have significant limitations in being generalized to other categories. In this article, we propose a multi-category anomaly editing network with a dual-branch training approach: one branch processes defect-free images (normal branch), while the other handles synthetic anomaly images (anomaly branch). Specifically, the paired samples are first fed into the multi-category anomaly feature editing based auto-encoder (MCAFE-AE) to perform image reconstruction and inpainting. In the normal branch, we propose a dual-entropy constrained deep embedded clustering module (DEC-DECM) to promote a more compact and orderly distribution of normal latent features, while avoiding trivial clustering solutions. Based on the clustering results, we further design a patch-based adaptive thresholding (PAT) strategy to adaptively calculate the threshold representing the central boundary of the cluster center for each local patch, thereby enabling the model to detect anomalies. Then, in the anomaly branch, we propose a multi-category anomaly feature editing module (MCAFEM) to identify anomalies in synthetic images and apply a category-oriented feature editing strategy to transform detected anomaly features into normal ones, thereby suppressing the reconstruction of anomalies. After completing the image reconstruction and inpainting, the input images from both branches and their respective output images are concatenated and fed into the correlation exploration and voxel-level attention based prediction network (CEVA-Net) for anomaly segmentation. The network is integrated with our proposed correlation-dependency exploration and voxel-level attention refinement module (CDE-VARM) and generates precise anomaly maps under the guidance of the bidirectional-path feature fusion (BPFF) and deep supervised learning (DSL). Extensive experiments on three datasets show that our method achieves state-of-the-art performance.

In recent years, linguistic generative steganography has been greatly developed. Current linguistic steganography methods can generate sufficiently fluent steganographic text, but cannot control the semantic expression of the generated steganographic text. In social network, steganographic text with inconsistent contextual semantics may pose the vigilance of the regulators. This paper proposes a controllable semantic linguistic steganography method via summarization generation. By analyzing the semantic content of the given text, a compact summarization that retains the most important content is generated, which can ensure the consistent semantics. During summarization generation, the secret information is embedded based on the dynamic programming algorithm to find optimal steganographic encoding path. Compared with the existing methods, the experimental results show that the proposed method has greater advantages in semantic consistency while ensuring security.

Image reconstruction-based methods with autoencoder have been widely used for unsupervised anomaly detection. By training the reconstruction on normal samples, autoencoder is supposed to produce higher reconstruction error for anomalous samples, which is used as an indicator for detecting anomalies. However, since autoencoder adopts the bottleneck layer to reconstruct data, it is hard to control its generalization capability. When the generalization capability is high, anomalous features can be confused with normal features, resulting in accurate reconstruction of anomalous regions as well. In this article, we propose a dual-constraint autoencoder to alleviate the problem of feature confusion through the dual constraint of adversarial learning and global memory bank. Given a sample pair consisting of a normal sample and a synthetic anomaly sample generated from it as input, the proposed autoencoder first forces the encoding of the synthetic anomaly image to be close to the normal encoding by adversarial learning. Then, the synthetic anomaly encoding is rerepresented with items in memory bank, which records normal features that facilitate image restoration, and the new feature is fed to the decoder for image inpainting. Under such constraints, the generalization capability of the autoencoder is suppressed and will not reconstruct the anomaly region well. After that, we feed the synthetic anomaly image together with the repaired image into the proposed adaptive weighted similarity spatial attention-based U-Net and produce an accurate anomaly map with the help of our proposed adaptive weighted similarity spatial attention. Results on the a comprehensive real-world dataset for unsupervised anomaly detection (MVTec AD) dataset and the beanTech anomaly detection dataset dataset show that our framework achieves state-of-the-art performance.