Text steganography has received a lot of attention in the application of covert communication. How to ensure desirable capacity and imperceptibility has become a key issue in text steganography. There are two typical approaches, i.e., text-selection-based steganography and text-generation-based steganography. However, the text-selection-based approaches generally have the very low hidden capacity and are not applicable in practical scenarios. Although the text-generation-based approaches can embed secret messages with higher capacity during text generation, they are prone to semantic incoherence and semantic errors when generating long texts. To address the abovementioned issues, this article proposes a novel text steganography based on long readable text generation. It first determines the topic of the stego-text according to the scenarios of the communication parties. Then, the plug and play language model (PPLM) is explored to generate the long readable stego-text conforming to the topic with semantic coherency. A given secret message is hidden during text generation by selecting proper words in an established embeddable candidate word pool (ECWP). Establishing the ECWP prevents the language model (LM) from selecting words with low probability in the text generation, thereby avoiding the generation of low-quality or even grammatically incorrect stego-text. Experimental results show that the proposed approach significantly increases hidden capacity while maintaining good imperceptibility compared with the existing approaches.

Abstract Digital image watermarking is one of the effective schemes to protect the copyrights of still images. However, the existing watermarking schemes are still not robust enough to the common geometric transformation attacks such as arbitrary rotation, scaling and shifting with desirable hiding capacity. To address this issue, we propose a robust watermarking scheme based on geometric correction codes (GCCs). In this scheme, the watermark and pre‐set GCCs are combined and embedded into a cover image to obtain the watermarked image. At the stage of watermark extraction, the watermarked image, under a variety of geometric transformation attacks, can be geometrically corrected by minimising the difference between the extracted and the original GCCs, then the watermark is extracted from the watermarked image. The experiments demonstrate that, compared to the typical watermarking schemes, the proposed scheme achieves much higher robustness to the common geometric transformation attacks and comparable invisibility with the same embedding capacity.

As a promising technique of resisting steganalysis detection, generative steganography usually generates a new image driven by secret information as the stego-image. However, it generally encodes secret information as entangled features in a non-distribution-preserving manner for the stego-image generation, which leads to two common issues: 1) limited accuracy of information extraction, and 2) low security in feature-domain. To address the above issues, we propose a generative steganographic framework via auto-generation of semantic object contours, in which a given secret message is encoded as the disentangled features,i.e., object-contours, in a distribution-preserving manner for the stego-image generation. In this framework, we propose a contour generative adversarial nets (CtrGAN) consisting of a contour-generator and a contour-discriminator, which are adversarially trained with reinforcement learning. To realize the generative steganography, by using the contour-generator of the trained CtrGAN, a contour point selection (CPS)-based encoding strategy is designed to encode the secret message as the contours. Then, the BicycleGAN is employed to transform the generated contours to the corresponding stego-image. Extensive experiments demonstrate the proposed steganographic approach achieves superior performance in the aspects of information extraction accuracy, especially under common image attacks, and feature-domain security, compared to the state-of-the-arts.

The purposes are to investigate how blockchain can solve the security problems in Intelligent Autonomous Transport System (IATS) and intelligentize the logistics transportation development. Regarding the scarcity of trust and concentration of rights caused by the centralized structure of traditional logistics information systems, a blockchain-based IATS is proposed. The system employs Ethereum as the underlying blockchain to record sensitive information, such as system orders, cargos, and personnel information on the blockchain, ensuring the non-tampering and credibility of data. Simultaneously, an order management module, a warehouse management module, a transportation management module, a transaction management module, and a system management module are established. In the meantime, the Light Gradient Boosting Machine (LightGBM) algorithm is utilized to recommend vehicle and cargo matching during transportation. Finally, the constructed algorithm model is simulated to analyze its performance. Results demonstrate that the security prediction accuracy of the proposed algorithm reaches 88.72%; moreover, the security prediction precision, recall, and F1 of the proposed algorithm are considerably better than those of other algorithms. Furthermore, the actual effect of each algorithm is analyzed. The LightGBM algorithm outperforms other algorithms and unused algorithms in click rate, conversion rate, turnover rate, and average response time. Therefore, the constructed blockchain-based IATS has excellent security performance and prediction accuracy, which provides an experimental basis for the later intelligent logistics transportation development.

As more and more image data are stored in the encrypted form in the cloud computing environment, it has become an urgent problem that how to efficiently retrieve images on the encryption domain. Recently, Convolutional Neural Network (CNN) features have achieved promising performance in the field of image retrieval, but the high dimension of CNN features will cause low retrieval efficiency. Also, it is not suitable to directly apply them for image retrieval on the encryption domain. To solve the above issues, this paper proposes an improved CNN-based hashing method for encrypted image retrieval. First, the image size is increased and inputted into the CNN to improve the representation ability. Then, a lightweight module is introduced to replace a part of modules in the CNN to reduce the parameters and computational cost. Finally, a hash layer is added to generate a compact binary hash code. In the retrieval process, the hash code is used for encrypted image retrieval, which greatly improves the retrieval efficiency. The experimental results show that the scheme allows an effective and efficient retrieval of encrypted images.