Research on digital image watermark encryption based on hyperchaos

The digital watermarking technique embeds meaningful information into one or more watermark images hidden in one image, in which it is known as a secret carrier. It is difficult for a hacker to extract or remove any hidden watermark from an image, and especially to crack so called digital watermark. The combination of digital watermarking technique and traditional image encryption technique is able to greatly improve anti-hacking capability, which suggests it is a good method for keeping the integrity of the original image. The research works contained in this thesis include: (1)A literature review the hyperchaotic watermarking technique is relatively more advantageous, and becomes the main subject in this programme. (2)The theoretical foundation of watermarking technologies, including the human visual system (HVS), the colour space transform, discrete wavelet transform (DWT), the main watermark embedding algorithms, and the mainstream methods for improving watermark robustness and for evaluating watermark embedding performance. (3) The devised hyperchaotic scrambling technique it has been applied to colour image watermark that helps to improve the image encryption and anti-cracking capabilities. The experiments in this research prove the robustness and some other advantages of the invented technique. This thesis focuses on combining the chaotic scrambling and wavelet watermark embedding to achieve a hyperchaotic digital watermark to encrypt digital products, with the human visual system (HVS) and other factors taken into account. This research is of significant importance and has industrial application value

FPGA based secure and noiseless image transmission using LEA and optimized bilateral filter

In today’s world, the transmission of secured and noiseless image is a difficult task. Therefore, effective strategies are important to secure the data or secret image from the attackers. Besides, denoising approaches are important to obtain noise-free images. For this, an effective crypto-steganography method based on Lightweight Encryption Algorithm (LEA) and Modified Least Significant Bit (MLSB) method for secured transmission is proposed. Moreover, a bilateral filter-based Whale Optimization Algorithm (WOA) is used for image denoising. Before image transmission, the secret image is encrypted by the LEA algorithm and embedded into the cover image using Discrete Wavelet Transform (DWT) and MLSB technique. After the image transmission, the extraction process is performed to recover the secret image. Finally, a bilateral filter-WOA is used to remove the noise from the secret image. The Verilog code for the proposed model is designed and simulated in Xilinx software. Finally, the simulation results show that the proposed filtering technique has superior performance than conventional bilateral filter and Gaussian filter in terms of Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM)

On the Infeasibility of Modeling Polymorphic Shellcode

Polymorphic malcode remains a troubling threat. The ability formal code to automatically transform into semantically equivalent variants frustrates attempts to rapidly construct a single, simple, easily verifiable representation. We present a quantitative analysis of the strengths and limitations of shellcode polymorphism and consider its impact on current intrusion detection practice. We focus on the nature of shellcode decoding routines. The empirical evidence we gather helps show that modeling the class of self-modifying code is likely intractable by known methods, including both statistical constructs and string signatures. In addition, we develop and present measures that provide insight into the capabilities, strengths, and weaknesses of polymorphic engines. In order to explore countermeasures to future polymorphic threats, we show how to improve polymorphic techniques and create a proof-of-concept engine expressing these improvements. Our results indicate that the class of polymorphic behavior is too greatly spread and varied to model effectively. Our analysis also supplies a novel way to understand the limitations of current signature-based techniques. We conclude that modeling normal content is ultimately a more promising defense mechanism than modeling malicious or abnormal content