In the existing deep learning based watermarking models, extracted image features for fusing with watermark are not abundant enough and more critically, essential features are not highlighted to be learned with the purpose of robust watermarking, both of which limit the watermarking performance. To solve those two drawbacks, this paper proposes an attention-guided robust image watermarking model based on generative adversarial network (ARWGAN). To acquire a great deal of representational image features, a feature fusion module (FFM) is devised to learn shallow and deep features effectively for multi-layer fusion with watermark, and meanwhile, reuse of those features by the dense connection enhances robustness. To alleviate image distortion caused by embedding watermark, an attention module (AM) is deployed to compute the attention mask by mining the global features of the original image. Specifically, with the guidance of the attention mask, image features representing inconspicuous regions and texture regions are enhanced for embedding the high strength of watermark, and simultaneously other features are suppressed to improve the watermarking performance. Furthermore, the noise sub-network is adopted for robustness enhancement by simulating various image attacks in iterative training. The discriminator is used to distinguish the encoded image from the original image for improving watermarking invisibility continuously. Experimental results demonstrate that ARWGAN is superior to the existing state-of-the-art watermarking models, and ablation experiments prove the effectiveness of the FFM and the AM. The code is avaliable in https://github.com/river-huang/ARWGAN.

The hybridoma cell screening method is usually done manually by human eyes during the production process for monoclonal antibody drugs. This traditional screening method has certain limitations, such as low efficiency and subjectivity bias. Furthermore, most of the existing deep learning-based image segmentation methods have certain drawbacks, due to different shapes of hybridoma cells and uneven location distribution. In this paper, we propose a deep hybridoma cell image segmentation method based on residual and attention U-Net (RA-UNet). Firstly, the feature maps of the five modules in the network encoder are used for multi-scale feature fusion in a feature pyramid form and then spliced into the network decoder to enrich the semantic level of the feature maps in the decoder. Secondly, a dual attention mechanism module based on global and channel attention mechanisms is presented. The global attention mechanism (non-local neural network) is connected to the network decoder to expand the receptive field of the feature map and bring more rich information to the network. Then, the channel attention mechanism SENet (the squeeze-and-excitation network) is connected to the non-local attention mechanism. Consequently, the important features are enhanced by the learning of the feature channel weights, and the secondary features are suppressed, hence improving the cell segmentation performance and accuracy. Finally, the focal loss function is used to guide the network to learn the hard-to-classify cell categories. Furthermore, we evaluate the performance of the proposed RA-UNet method on a newly established hybridoma cell image dataset. Experimental results show that the proposed method has good reliability and improves the efficiency of hybridoma cell segmentation compared with state-of-the-art networks such as FCN, UNet, and UNet++. The results show that the proposed RA-UNet model has improvements of 0.8937%, 0.9926%, 0.9512%, and 0.9007% in terms of the dice coefficients, PA, MPA, and MIoU, respectively.

This paper proposes a screen-shooting resilient watermarking scheme via learned invariant keypoints and QT; that is, if the watermarked image is displayed on the screen and captured by a camera, the watermark can be still extracted from the photo. A screen-shooting resilient watermarking algorithm should meet the following two basic requirements: robust keypoints and a robust watermark algorithm. In our case, we embedded watermarks by combining the feature region filtering model to SuperPoint (FRFS) neural networks, quaternion discrete Fourier transform (QDFT), and tensor decomposition (TD). First we applied FRFS to locate the embedding feature regions which are decided by the keypoints that survive screen-shooting. Second, we structured watermark embedding regions centered at keypoints. Third, the watermarks were embedded by the QDFT and TD (QT) algorithm, which is robust for capturing process attacks. In a partial shooting scenario, the watermark is repeatedly embedded into different regions in an image to enhance robustness. Finally, we extracted the watermarks from at least one region at the extraction stage. The experimental results showed that the proposed scheme is very robust for camera shooting (including partial shooting) different shooting scenarios, and special attacks. Moreover, the efficient mechanism of screen-shooting resilient watermarking could have propietary protection and leak tracing applications.

QR code (quick response code) is used due to its beneficial properties, especially in the mobile payment field. However, there exists an inevitable risk in the transaction process. It is not easily perceived that the attacker tampers with or replaces the QR code that contains merchant’s beneficiary account. Thus, it is of great urgency to conduct authentication of QR code. In this study, we propose a novel mechanism based on visual cryptography scheme (VCS) and aesthetic QR code, which contains three primary schemes for different concealment levels. The main steps of these schemes are as follows. Firstly, one original QR code is split into two shadows using VC multiple rules; secondly, the two shadows are embedded into the same background image, respectively, and the embedded results are fused with the same carrier QR code, respectively, using XOR mechanism of RS and QR code error correction mechanism. Finally, the two aesthetic QR codes can be stacked precisely and the original QR code is restored according to the defined VCS. Experiments corresponding to three proposed schemes are conducted and demonstrate the feasibility and security of the mobile payment authentication, the significant improvement of the concealment for the shadows in QR code, and the diversity of mobile payment authentication.

Robust reversible watermarking in an encrypted domain is a technique that preserves privacy and protects copyright for multimedia transmission in the cloud. In general, most models of buildings and medical organs are constructed by three-dimensional (3D) models. A 3D model shared through the internet can be easily modified by an unauthorized user, and in order to protect the security of 3D models, a robust reversible 3D models watermarking method based on homomorphic encryption is necessary. In the proposed method, a 3D model is divided into non-overlapping patches, and the vertex in each patch is encrypted by using the Paillier cryptosystem. On the cloud side, in order to utilize addition and multiplication homomorphism of the Paillier cryptosystem, three direction values of each patch are computed for constructing the corresponding histogram, which is shifted to embed watermark. For obtaining watermarking robustness, the robust interval is designed in the process of histogram shifting. The watermark can be extracted from the symmetrical direction histogram, and the original encrypted model can be restored by histogram shifting. Moreover, the process of watermark embedding and extraction are symmetric. Experimental results show that compared with the existing watermarking methods in encrypted 3D models, the quality of the decrypted model is improved. Moreover, the proposed method is robust to common attacks, such as translation, scaling, and Gaussian noise.

With high performances of image capturing tools, image information can be easily obtained by screenshots that make image copyright protection a challenging task. The existing screen-shooting watermarking algorithms suffer from a huge running time, in addition to their low robustness against different screenshot attacks, such as different distances and capturing angles of the screenshots. In this paper, a fast and robust high-capacity flexible watermarking algorithm for screenshot images is proposed. Firstly, Oriented FAST and Rotated BRIEF (ORB) feature points are extracted from the input image. Secondly, the feature points are then sorted in a descending order according to their response values. Then, the first five non-overlapping feature points are selected for the embedding by using Hamming window-based filtering method. Furthermore, we exploit the multi-resolution property of Discrete Wavelet Transform (DWT) and energy compaction property of Singular Value Decomposition (SVD) to embed the watermark. Therefore, the classical DWT combined with Singular Value Decomposition (SVD) are adopted to improve the robustness and capacity of the proposed watermarking algorithm. At the extraction side, the sum of the response values for the three RGB channels of the color-ripped image is calculated to improve the feature point localization accuracy. Experimental results show that the proposed screen-shooting watermarking algorithm improves running speed while ensuring the robustness. Furthermore, it has less time complexity and high robustness compared with the state-of-the-art watermarking algorithms against different screenshot attacks.