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.

The classic scrambling-diffusion structure for image encryption is vulnerable to be attacked. The scrambling and diffusion process can be cracked separately due to the two processes are independent. In this paper, we proposed a novel color image encryption algorithm which can overcome the problem. In the proposed algorithm, firstly, the first diffusion is performed simultaneously with the first scrambling which can resist the attacker to separately crack the scrambling or diffusion. Then, the second diffusion matrix generated from the Hopfield chaotic neural network (HCNN) which is related to the plaintext is performed which can not only resist common chosen-plaintext attack, but also improved the key sensitivity. Finally, a second scrambling is performed on some special pixels to further improve the security. Experiments and security analysis indicate that the proposed algorithm has an excellent performance in image encryption and various attacks.

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 HNN definition and its complex dynamics is 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 256×256 image size, an encrypted and compressed output image is obtained at the compression rate of 0.5, processing time of 0.147ms, encryption throughput of 1783.3MBits/s, for a 2.9GHz processor, the number of cycle to process the algorithm is 1.62. Consequently, the proposal can be applied to Wireless Body Area Networks (WBANs).

Currently, in consumer electronics industry, quite a number of E-healthcare products highly depend on consumers’ facial biometrics for both disease diagnosis and identity authen-tication. In light of this, various facial privacy-preserving approa-ches oriented towards the internet of medical things have been proposed, including visual cryptography and desensitization tech-nique. Nevertheless, few attentions have been paid to investigating their countermeasure, i.e., facial privacy analysis, owning to significant distribution discrepancy between the original and anony-mized facial images. To this end, a general Denoising Diffusion Cryptanalytic Model (DDCM) is proposed in this paper to recon-struct the original facial images in a coarse-to-fine manner from their encrypted ones with diverse anonymous approaches. Spec-ifically, to accurately reconstruct the original facial image without any prior knowledge, a customized initial prediction module with an encoder-decoder structure is introduced by taking advantage of its leaked visual cues to generate the coarse face. Furthermore, the residual map between the original and coarse facial images is explicitly modeled with the output of the customized prediction module as a conditional prior, thereby obtaining a refined facial image. Finally, extensive experiments demonstrate that the propo-sed DDCM could not only effectively crack multiple handcrafted facial anonymization approaches, such as blurring, pixelation and thumbnail-preserving encryption, but also outperform the prev-ious works (e.g., Pix2Pix) in terms of image fidelity and attack success rate.

In this paper, a novel double-image compression-encryption algorithm is proposed by combining Rossler hyperchaos and compressive sensing. In the proposed scheme, the sparse matrixes obtained by performing two-dimensional discrete wavelet transform on two plain images are first scrambled and compressed by index confusion and compressive sensing. Next, the compressed matrixes are linearly quantized, and then variable step length Josephus confusion and bitwise exclusive-OR operation are performed on them. The final noise-like cipher image is generated by hiding half of the encrypted image into the alpha channel of the other half of encrypted image. In order to realize “One cipher image corresponds to one key”, a novel key generation mechanism is designed. Finally, the simulation analyses indicate that the proposed double-image encryption scheme has high transmission efficiency. Meanwhile, the average local information entropy of the cipher image is about 0.902 and the decryption quality is as high as 30 dB.