Generative image steganography has attracted significant attention for its exceptional resistance to steganalysis. However, current generative steganography methods still face limitations in terms of the lack of provable security guarantees under statistical analysis and vulnerability to real-world, unforeseen channel attacks. To address these issues, this paper proposes a novel generative image steganography framework that leverages the Latent Diffusion Model (LDM). Notably, we have uncover a consistent trend: regardless of whether an image has undergone attacks such as compression or noise addition, the sign pattern of values in its latent vector encoded by the LDM remains largely invariant. Capitalizing on this trend, we have devised an adaptive distribution-preserving mapping (ADPM) mechanism, capable of converting a secret message into a latent vector that follows standard normal distribution in an adjustable way. Since both the secret latent vector and the latent vector randomly generated during regular image generation follow the same distribution, satisfying the optimal input conditions for the diffusion model, the proposed method can achieve provable security. Experimental results demonstrate the outstanding performance of our approach in terms of robustness, security, and extraction accuracy.

In recent years, generative steganography has witnessed remarkable progress in the field of covert communication. It leverages techniques such as generative adversarial networks (GANs) or flow-based generative models (GLOW) to generate stego images. However, these approaches often grapple with the dilemma of achieving optimal steganographic capacity while ensuring the accurate extraction of hidden information. Additionally, the models occasionally still generate low-quality images that are highly vulnerable to detection by steganalysis tools. To tackle the aforementioned challenges and enhance the overall performance of generative image steganography, this paper proposes the deterministic guided additive diffusion model for generative image steganography (DGADM-GIS). Initially, we devise a reversible mapping function that is used for deterministic guided by a provided secret message, and then construct a secret latent Gaussian vector. Moreover, the proposed DGADM-GIS framework designs an additive sampling method based on the superposition principle of normal distribution to obtain a Gaussian vector that satisfies independent, random and obeys the standard normal distribution, which is transformed to a stego image in a way of maintaining the distribution by the diffusion model. Furthermore, we conduct error analysis experiments on our proposed scheme and derive methods to enhance the accuracy of secret information extraction. The experimental results show that our proposed steganographic method exhibits robust resistance to steganalysis. When embedding 3 bits of secret information per pixel, it achieves nearly 100% extraction accuracy.

Presentation Attack Detection Model (PADM) plays a crucial role in biometric authentication, particularly in fingerprint Liveness Detection Model (FLDM). However, FLDM is susceptible to various unauthorized usage and dissemination threats, highlighting the urgent necessity to protect its Intellectual Property (IP). While previous backdoor watermarking techniques have been effective in authenticating the IP of FLDM, they unfortunately pose a risk to its performance. Thus, this paper presents a novel model watermarking strategy named CLGuard, leveraging robust fingerprint attributes to create authentic label watermarks, facilitating the verification of ownership for FLDM. Notably, these watermarks seamlessly integrate into both the input and output layers, minimizing any irreversible modifications to the feature space of FLDM, ensuring optimal performance. Initially, a selection of High Complexity Inputs (HCI) is chosen based on robust fingerprint attributes, and watermark data is embedded into the HCI set using imperceptible backdoor. Simultaneously, a coding network is deployed to conceal the watermark data within the background layer of the samples, mitigating any adverse impacts on the model's deep feature space. Subsequently, a mapping function is established between triggers and original labels, enabling the fine-tuning of the model with embedded watermarks on a diverse mixed dataset. Ultimately, IP is authenticated by leveraging trigger sets and original misidentification sets. Comprehensive experiments on multiple benchmark datasets demonstrate that CLGuard effectively authenticates suspicious PADM IP without introducing anomalous input-output pairs, thus preserving the original task performance. Furthermore, it withstands attacks such as fine-tuning, neuronal cleanse, and watermark removal, showcasing reliability, stealthiness, fidelity, and robustness.

The decline of cultivated land significantly threatens the food supply. In recent years, remote sensing (RS) change detection emerged as a valuable tool for monitoring nonagriculturalization. However, existing deep learning (DL) methods for change detection suffer from the problem of inadequate utilization of feature information during image feature extraction, leading to noisy or inaccurate change maps. To address these challenges, we propose a Siamese network based on full-scale connected UNet (SNUNet3+), whose encoder extracts features from the original images at various levels. Then, it merges all fine-grained spatial information and coarse-grained semantic information into the decoder through full-scale skip connections to obtain the feature maps with complete information. The spatial and channel squeeze and excitation (scSE) attention mechanism is embedded within the decoder subnetwork to enhance the discriminative power of the features. In addition, a deep supervision module is introduced to improve the feature learning capability of the hidden layers and the quality of features. Moreover, accurate cultivated land change detection remains challenging because of the lack of fine-grained detection datasets. To enable the proposed method to achieve cultivated land change detection, we produced a cultivated land change detection dataset containing 5170 pairs of 256$\times $256 bitemporal images with a spatial resolution of 1 m. The effectiveness and performance of the proposed method are evaluated on three high-resolution RS change detection datasets. Extensive experimental results show that the proposed method outperforms other state-of-the-art methods, achieving the highest${F}1$-score of 72.90%, 90.36%, and 96.64% on the CLCD dataset, LEVIR-CD dataset, and PX-CLCD dataset, respectively.