Crack detection, particularly from pavement images, presents a formidable challenge in computer vision due to inherent complexities such as intensity inhomogeneity, intricate topologies, low contrast, and noisy backgrounds. Automated crack detection is crucial for maintaining the structural integrity of essential infrastructures, including buildings, pavements, and bridges. Existing lightweight methods often face challenges, including computational inefficiency, complex crack patterns, and difficult backgrounds, leading to inaccurate detection and impracticality for real-world applications. We propose EfficientCrackNet, a lightweight hybrid model combining Convolutional Neural Networks (CNNs) and transformers for precise crack segmentation to address these limitations. EfficientCrackNet integrates depthwise separable convolutions (DSC) layers and MobileViT block to capture global and local features. The model employs an Edge Extraction Method (EEM) for efficient crack edge detection without pretraining and an Ultra-Lightweight Subspace Attention Module (ULSAM) to enhance feature extraction. Extensive experiments on three benchmark datasets, Crack500, DeepCrack, and GAPs384, demonstrate that EfficientCrackNet achieves superior performance compared to existing lightweight models, requiring only 0.26M parameters and 0.483 GFLOPs. The proposed model offers an optimal balance between accuracy and computational efficiency, outperforming state-of-the-art lightweight models and providing a robust and adaptable solution for real-world crack segmentation.

ABSTRACT Traditional codebooks used for tracing information leakage in text documents often suffer from limitations in embedding capacity, robustness, and efficiency due to their manual generation process. This paper proposes a robust text watermarking method based on the stroke components of Chinese characters. By designing an innovative approach, Chinese character strokes are divided into several distinct components, with only specific ones being selectively modified to generate new glyphs, thus forming a unique codebook. The watermark signals are embedded by substituting the carrier glyph with the newly generated one, and the signals are extracted using a template matching method. Experimental results demonstrate that, compared to traditional manually designed codebooks, the proposed method significantly reduces human labor and computational overhead while maintaining high visual quality. Moreover, it exhibits superior robustness and adaptability across various challenging scenarios, including digital noise attacks, print‐scanning attacks, and print‐camera capture, making it a highly effective solution for protecting textual information.

Watermarking by Zernike moments has been proven to be effective in providing high rotational resistance. However, due to the high computational complexity, the conventional video watermarking methods using Zernike moments are developed for videos with low resolution. Moreover, according to the properties of Zernike moments, only the matrices of equal height and width can be calculated since the inscribed circle of the original image matrix is selected as the area to be processed, but most of the available videos on the Internet do not meet such requirement. To solve the above problem, this paper proposes a fast watermarking method based on Zernike moments for high resolution videos to resist various attacks. In the proposed method, the frames of a video sequence are firstly grouped, from which a certain number of frame pairs are then selected for watermark embedding. For each frame pair to be embedded, we partition one frame into a set of disjoint blocks and apply singular value decomposition to each block to obtain a square feature matrix. Thereafter, by calculating all the Zernike moments, secret information is embedded into the selected Zernike moments to achieve superior robustness while keeping imperceptibility. Finally, according to the video encoding framework, we overwrite the frame difference of the frame pair by the watermark to resist compression and transcoding attacks. Furthermore, we propose two optional methods for compensating the special cases of rotation and scaling attacks during watermark detection. Experiments demonstrate the advantage of our method over the existing robust watermarking methods.

In recent years, with the significant improvement of Gener-ative Adversarial Networks (GANs), fake images generated by GAN become hardly distinguishable from real ones, thus threatening the authentication of digital images. To resolve this issue, several fake image detectors based on supervised binary classification have been designed. However, current methods remain vulnerable when testing samples are gener-ated by an unknown GAN model. In this work, an unsuper-vised domain adaptation strategy is introduced to improve the performance in the generalization of GAN-generated image detection by using a small number of unlabeled images from the target domain. Self-Attention block and novel loss function have been constructed to optimize the domain adaptation process, thus getting a better generalization. Experimental results demonstrate that the proposed scheme achieves high detection accuracy with few unlabeled images in the target domain, which shows that unsupervised methods can be used for the detection of GAN-generated images.

Abstract The segmentation of building from satellite and airborne images is necessary for high‐resolution buildings maps generation and it is still challenging. On annotated pixel‐level images, trained deep convolutional neural networks (CNNs) were used to improve segmentation of building. The cost of labelling training data is high, which reduces their usage. Human labelling efforts can be significantly reduced using weakly supervised segmentation techniques. Here, a novel weakly supervised framework is introduced for building semantic segmenting that relies on deep seeds to construct a superpixels‐CRF model over superpixels segmentation in order to generate high‐quality initial pixel‐level annotations, as the initialization step. Then, the segmentation network is trained using the initial pixel‐level annotations. Next, the CRF model is used to refine the segmentation masks, and the segmentation network is retrained to achieve accurate pixel‐level annotations while iteratively optimizing the segmentation. The experimental results on three public building datasets demonstrate that the proposed framework significantly improved the quality of building semantic segmentation while remaining computationally efficient.

With the increasing popularity of digital video communication, video data hiding has become an active research topic in covert communication and privacy protection. Traditional video data hiding methods often use quantized discrete cosine transform (QDCT) coefficients to carry a sufficient payload. However, since QDCT coefficients expose texture features and motion characteristics of the present video frame heavily, data embedding with QDCT coefficients may lead to significant intra-frame distortion and inter-frame distortion drift. To avoid obvious visual artifacts and keep bit-rate within a satisfactory level of the marked video, data embedding in QDCT coefficients should take into account both the intra-frame and inter-frame distortion impacts. It motivates the authors to propose an efficient cost assignment-based video data hiding method in this paper. The proposed cost assignment method aims to accurately evaluate the data embedding distortion. Specifically, the proposed scheme considers intra-frame changes and intra-frame distortion drift, for which the texture and motion changes of frames can be measured. The frame position is also used to reflect a cumulative distortion difference of multiple frames. For data embedding, syndrome-trellis code (STC) is adopted to minimize the overall distortion. Experimental results show that the proposed method significantly outperforms existing works in terms of payload-distortion performance.