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.

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.

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.

To protect the copyright of the color image, a color image watermarking scheme based on quaternion discrete Fourier transform (QDFT) and tensor decomposition (TD) is presented. Specifically, the cover image is partitioned into non-overlapping blocks, and then QDFT is performed on each image block. Then, the three imaginary frequency components of QDFT are used to construct a third-order tensor. The third-order tensor is decomposed by Tucker decomposition and generates a core tensor. Finally, an improved odd–even quantization technique is employed to embed a watermark in the core tensor. Moreover, pseudo-Zernike moments and multiple output least squares support vector regression (MLS–SVR) network model are used for geometric distortion correction in the watermark extraction stage. The scheme utilizes the inherent correlations among the three RGB channels of a color image, and spreads the watermark into the three channels. The experimental results indicate that the proposed scheme has better fidelity and stronger robustness for common image-processing and geometric attacks, can effectively resist each color channel exchange attack. Compared with the existing schemes, the presented scheme achieves better performance.

Mobile devices have been increasingly used to take pictures without leaving a trace. However, the application system can lead to confidential information leaks. A framework for screen-shooting-resilient watermarking via deep networks (SSDeN) in the frequency domain is put forward in this study to solve this problem. The proposed framework can extract the watermark from the leaked photo for copyright protection. SSDeN is an end-to-end process that combines convolutional neural network (CNN) with residual block to embed and extract watermarks in the DCT domain. We simulate some screen-shooting attacks to ensure the networks embed the watermark robustly. Our framework achieves the state-of-the-art performance on existing learning architectures for screen-shooting-resilient watermarking.