Secret image sharing (SIS), as one of the applications of information theory in information security protection, has been widely used in many areas, such as blockchain, identity authentication and distributed cloud storage. In traditional secret image sharing schemes, noise-like shadows introduce difficulties into shadow management and increase the risk of attacks. Meaningful secret image sharing is thus proposed to solve these problems. Previous meaningful SIS schemes have employed steganography to hide shares into cover images, and their covers are always binary images. These schemes usually include pixel expansion and low visual quality shadows. To improve the shadow quality, we design a meaningful secret image sharing scheme with saliency detection. Saliency detection is used to determine the salient regions of cover images. In our proposed scheme, we improve the quality of salient regions that are sensitive to the human vision system. In this way, we obtain meaningful shadows with better visual quality. Experiment results and comparisons demonstrate the effectiveness of our proposed scheme.

In recent years, the wide application of deep neural network models has brought serious risks of intellectual property rights infringement. Embedding a watermark in a network model is an effective solution to protect intellectual property rights. Although researchers have proposed schemes to add watermarks to models, they cannot prevent attackers from adding and overwriting original information, and embedding rates cannot be quantified. Therefore, aiming at these problems, this paper designs a high embedding rate and tamper-proof watermarking scheme. We employ wet paper coding (WPC), in which important parameters are regarded as wet blocks and the remaining unimportant parameters are regarded as dry blocks in the model. To obtain the important parameters more easily, we propose an optimized probabilistic selection strategy (OPSS). OPSS defines the unimportant-level function and sets the importance threshold to select the important parameter positions and to ensure that the original function is not affected after the model parameters are changed. We regard important parameters as an unmodifiable part, and only modify the part that includes the unimportant parameters. We selected the MNIST, CIFAR-10, and ImageNet datasets to test the performance of the model after adding a watermark and to analyze the fidelity, robustness, embedding rate, and comparison schemes of the model. Our experiment shows that the proposed scheme has high fidelity and strong robustness along with a high embedding rate and the ability to prevent malicious tampering.

Open source software supply-chain attacks, once successful, can exact heavy costs in mission-critical applications. As open source ecosystems for deep learning flourish and become increasingly universal, they present attackers previously unexplored avenues to code-inject malicious backdoors in deep neural network models. This article proposes Flareon , a small, stealthy, seemingly harmless code modification that specifically targets the data augmentation pipeline with motion-based triggers. Flareon neither alters ground-truth labels, nor modifies the training loss objective, nor does it assume prior knowledge of the victim model architecture, training data, and training hyperparameters. Yet, it has a surprisingly large ramification on training—models trained under Flareon learn powerful target-conditioned (or “ all2all ”) backdoors. We also proposed a learnable variant of Flareon that is even stealthier in terms of added perturbations. The resulting models can exhibit high attack success rates for any target choices and better clean accuracies than backdoor attacks that not only seize greater control but also assume more restrictive attack capabilities. We also demonstrate the resilience of Flareon against a wide range of defenses. Flareon is fully open source and available online to the deep learning community.

Synthetic aperture radar (SAR) has been widely used in recent years, and SAR automatic target recognition (ATR) has become a research hotspot. Most of the existing SAR ATR methods focus on the network structure design and increasing data volume but omit image quality and real-time processing. We design a multimodule image enhancement network (MMIE-Net) to solve these problems, which include the target extraction module, the image processing module, and convolutional neural networks (CNNs). First, we use the target extraction module and the image processing module to enhance the quality of raw SAR images. Then we design a suitable network for SAR image recognition, which is simple, lightweight, and recognizable. The experiment was mainly carried out on the MSTAR dataset, which can be divided into two categories, Standard Operating Condition (SOC) and Extended Operating Condition (EOC). The identification accuracy, the parameter storage space, and the depth of the model are considered as the criterion. The experimental results show that, compared with other methods, the proposed method not only ensures the simple structure of the network model but also has better recognition accuracy. Additionally, our method is robust and stable to large depression angle variation as well.