Abstract Designing novel and effective distortion functions for spatial image steganography has become increasingly challenging. The controversial pixels prior (CPP) rule mitigates this by fusing existing distortion functions rather than constructing new ones, but it is limited to functions with comparable security performance. We propose G-CPP, a generalized fusion framework based on the grading of controversial pixels. G-CPP assigns embedding priorities according to pixel conflict levels, enabling more effective utilization of high-potential embedding locations. Furthermore, we introduce a customized scheduling strategy for the G-CPP and cost-spreading rules, tailored to the intrinsic properties of different distortion-function combinations, thereby enhancing statistical undetectability against both conventional and deep learning–based steganalyzers. G-CPP preserves the advantages of CPP while extending its applicability to functions with significantly divergent security levels. Experiments on multiple benchmark datasets show that G-CPP consistently outperforms the conventional CPP rule, achieving superior security performance across diverse function combinations.

With the increasing reliance on the biometric-based authentication systems, such as face recognition, in applications within the IoT and edge networks, guaranteeing proper service functionality while safeguarding individual biometric privacy has become a critical concern. However, most existing face privacy protection approaches mainly focus on preserving the machine-recognizable identity information, inadvertently compromising individual privacy. To tackle this challenge, a novel Face Privacy-Enhancing Network (FPE-Net) is proposed, which consists of two primary stages: biometric encryption and face reconstruction. Specifically, a linear encryption module is designed in the first stage for obfuscating the original identity information, which is later integrated into the depth features of the target face via an identity injector. Notably, the identity encryption process operates independently of the deep generative network, enabling greater flexibility and efficiency for key configuration. Then in the second stage, a face decoder is utilized to synthesize the photo-realistic face. Moreover, such face not only prevents cross-matching with biometric databases but also preserves recognition utility, owing to the linear encryption mechanism and loss design. Extensive quantitative and qualitative experimental results demonstrate the feasibility of FPE-Net model, which outperforms existing state-of-the-art approaches in terms of privacy protection.

Recent achievements have shown that model-based steganographic schemes hold promise for better security than heuristic-based ones, as they can provide theoretical guarantees on secure steganography under a given statistical model. However, it remains a challenge to exploit the correlations between DCT coefficients for secure steganography in practical scenarios where only a single compressed JPEG image is available. To cope with this, we propose a novel model-based steganographic scheme using the Conditional Random Field (CRF) model with four-element cross-neighborhood to capture the dependencies among DCT coefficients for JPEG steganography with symmetric embedding. Specifically, the proposed CRF model is characterized by the delicately designed energy function, which is defined as the weighted sum of a series of unary and pairwise potentials, where the potentials associated with the statistical detectability of steganography are formulated as the KL divergence between the statistical distributions of cover and stego. By optimizing the constructed energy function with the given payload constraint, the non-independent distortion cost corresponding to the least detectability can be accordingly obtained. Extensive experimental results validate the effectiveness of our proposed scheme, especially outperforming the previous independent art J-MiPOD.

Recent advances in intelligent wearable devices have brought tremendous chances for the development of healthcare monitoring system. However, the data collected by various sensors in it are user-privacy-related information. Once the individuals’ privacy is subjected to attacks, it can potentially cause serious hazards. For this reason, a feasible solution built upon the compression-encryption architecture is proposed. In this scheme, we design an Adaptive Sparse Basis Compressive Sensing (ASB-CS) model by leveraging Singular Value Decomposition (SVD) manipulation, while performing a rigorous proof of its effectiveness. Additionally, incorporating the Parametric Deformed Exponential Rectified Linear Unit (PDE-ReLU) memristor, a new fractional-order Hopfield neural network model is introduced as a pseudo-random number generator for the proposed cryptosystem, which has demonstrated superior properties in many aspects, such as hyperchaotic dynamics and multistability. To be specific, a plain medical image is subjected to the ASB-CS model and bidirectional diffusion manipulation under the guidance of the key-controlled cipher flows to yield the corresponding cipher image without visual semantic features. Ultimately, the simulation results and analysis demonstrate that the proposed scheme is capable of withstanding multiple security attacks and possesses balanced performance in terms of compressibility and robustness.

Chaos systems find extensive applications in cryptography and pseudorandom number generation due to their ability to generate pseudo-random signals. This paper focuses on enhancing the complexity of chaotic systems by introducing the memristor, a nonlinear component. We propose a novel map called the 2D memristive Cubic map (2D-MCM), which integrates the memristor with the Cubic map to create a discrete mapping. The 2D-MCM exhibits rich dynamical behavior and a broad parameter space. Notably, the 2D-MCM displays boosting bifurcation behavior. As the control parameters increase, the 2D-MCM demonstrates an expanded range of values, indicating its ability to generate a larger number of pseudo-random sequences. To validate its performance, we establish a hardware platform to physically capture the attractors of the 2D-MCM. To verify the performance of the 2D-MCM in generating pseudorandom sequences, we designed a video encryption algorithm based on the 2D-MCM. This algorithm selectively encrypts specific areas within the video, with correlation coefficients of the encrypted video in the horizontal, vertical, and diagonal directions being 0.0002, -0.0005, and 0.0004, respectively. Through simulation experiments and security analysis, we demonstrate that the 2D-MCM performs well in video encryption tasks.

Privacy preservation and low-cost data processing have become two critical issues in the era of Internet of Things (IoT) due to the widespread deployment of lightweight smart surveillance and sensors. In this article, we propose a multitiered reversible data privacy preservation system based on compressive sensing (CS) and watermarking. The system anonymizes the region of interest (ROI) using an obfuscation matrix while compressing and encrypting the entire document. CS provides the first-tier encryption for data documents, and the obfuscation matrix provides the second-tier encryption for sensitive parts of data documents. The system offers a multitiered privacy protection scheme where restricted-authorized users can only access nonsensitive data while fully authorized users can access the entire document. To implement the reversible elimination of the obfuscation matrix, two watermark embedding methodologies are proposed in the CS domain in order. In both methods, the watermark generated by the obfuscation matrix is embedded in the encrypted CS measurement values, with the first methodology concentrating on the optimal data reconstruction quality and the second methodology working to balance storage space and data restoration quality. Extensive experimental results indicate the superiority of the proposed methodologies over other conventional reversible data privacy preservation schemes.

Wireless body area network (WBAN) is a crucial tool in modern medical areas. This refers to the intelligent interconnection of wireless sensor nodes installed outside or inside the human body to recover some human vital signs. The security and bandwidth saturation remain crucial problems of this technology. In this work, a cross-ring-based neural network model (CRNN) is obtained from the Hopfield neural network definition and its complex dynamics are deeply analyzed. The steady-state study shows that the model has a unique equilibrium point and its analysis shows that the model’s dynamics is self-excited. Based on the two-parameter Lyapunov spectrum, the set of synaptic weights has been quickly identified to illustrate hyperchaotic behavior of the CRNN. With the help of bifurcation plots, graphs of the Lyapunov spectrum, and phase portraits, we have characterized periodic, chaotic, and hyperchaotic patterns in the model. Furthermore, an experimental setup has been built using microcontroller technology to further support the results of the numerical simulations. A parallel compressive sensing algorithm combined with a nonlinear congruent generator has been used to show the application of the newly designed CRNN to medical image compression and security. Security and compression performances indicate an efficient scheme with the capability to resist various attacks and the capability to produce low-size data image from large-size data image. For instant from$256\times 256 $image size, an encrypted and compressed output image is obtained at the compression rate of 0.5, processing time of 0.147 ms, encryption throughput of 1783.3 MBits/s, for a 2.9-GHz processor, the number of cycles to process the algorithm is 1.62. Consequently, the proposal can be applied to WBANs.

Abstract A new two‐dimensional chaotic system in the form of a cascade structure is designed, which is derived from the Chebyshev system and the infinite collapse system. Performance analysis including trajectory, Lyapunov exponent and approximate entropy indicate that it has a larger chaotic range, better ergodicity and more complex chaotic behaviour than those of advanced two‐dimensional chaotic system recently proposed. Moreover, to protect the security of the crowd image data, the newly designed two‐dimensional chaotic system is utilized to propose a visually meaningful image cryptosystem combined with singular value decomposition and Bernstein polynomial. First, the plain image is compressed by singular value decomposition, and then encrypted to the noise‐like cipher image by scrambling and diffusion algorithm. Later, the steganographic image is obtained by randomly embedding the cipher image into a carrier image in spatial domain through the Bernstein polynomial‐based embedding method, thereby realizing the double security of image information and image appearance. Besides, the visual quality of the steganographic image can be improved by the adjustment factor according to different carrier images during the embedding process. Ultimately, security analyses indicate that it has higher encryption efficiency (2 Mbps) and the visual quality of steganography image can reach 39 dB.