Visually Meaningful Image Encryption Scheme Based on DWT and Schur Decomposition

Steganography is a method of hiding sensitive information within an ordinary file to avoid detection. When the file is received, the hidden information is extracted from the original file. Various types of files can be used to conceal the information, including images, audio files, video files, and text files. This research paper focuses on the use of image files as the medium. To create a stego-image, a secret image is embedded into a cover image. Both the cover and secret images are subjected to a Daubechies 3 Discrete Wavelet Transformation (DWT), then encrypted using Arnold's Cat Map algorithm and fused using a simple alpha blending technique with the cover image. DWT enhances the invisibility of hidden information, making it more challenging to detect by visual inspection or statistical analysis. Arnold’s cat map adds an extra layer of security, making it more difficult for an attacker to extract the message. Alpha blending helps to maintain the visual quality and integrity of the cover image, reducing the chances of noticeable artifacts or anomalies that may arise due to the embedding process. The resulting stego-image appears visually like the original cover image, making it less suspicious to potential attackers. Experimental results have been made on multiple area such as the best value of alpha, which DWT family to use, and different combinations of images as cover and payload images. The results demonstrate that our proposed hybrid model achieves a high level of security, robustness, and imperceptibility in image steganography. Moreover, it outperforms existing methods in terms of image quality metrics, such as achieving a Peak Signal-to-Noise Ratio (PSNR) value of up to 70.8 and a Structural Similarity Index (SSIM) value of up to 0.98982. These metrics indicate that the stego-image retains a remarkable level of visual fidelity and similarity to the cover image, thereby enhancing the overall effectiveness of the steganography technique.

A reversible extended secret image sharing scheme based on Chinese remainder theorem

An enhanced hybrid image watermarking scheme for security of medical and non-medical images based on DWT and 2-D SVD

WITHDRAWN: Contrasting the performance metrics of discrete transformations on digital image steganography using artificial intelligence

This paper proposes a new image compression-encryption algorithm based on a meaningful image encryption framework. In block compressed sensing, the plain image is divided into blocks, and subsequently, each block is rendered sparse. The zigzag scrambling method is used to scramble pixel positions in all the blocks, and subsequently, dimension reduction is undertaken via compressive sensing. To ensure the robustness and security of our algorithm and the convenience of subsequent embedding operations, each block is merged, quantized, and disturbed again to obtain the secret image. In particular, landscape paintings have a characteristic hazy beauty, and secret images can be camouflaged in them to some extent. For this reason, in this paper, a landscape painting is selected as the carrier image. After a 2-level discrete wavelet transform (DWT) of the carrier image, the low-frequency and high-frequency coefficients obtained are further subjected to a discrete cosine transform (DCT). The DCT is simultaneously applied to the secret image as well to split it. Next, it is embedded into the DCT coefficients of the low-frequency and high-frequency components, respectively. Finally, the encrypted image is obtained. The experimental results show that, under the same compression ratio, the proposed image compression-encryption algorithm has better reconstruction effect, stronger security and imperceptibility, lower computational complexity, shorter time consumption, and lesser storage space requirements than the existing ones.

Joint adjustment image steganography networks

Steganography is the art and science of covert communication. The secret information can be concealed in content such as image, audio, or video. This paper provides a novel image steganography technique to hide both image and keyin color cover image using Discrete Wavelet Transform (DWT).There is no visual difference between the stego image and the cover image. The extracted image is also similar to the secret image. This is proved by the high PSNR (Peak Signal to Noise Ratio), value for both stego and extracted secret image. The results are compared with the results of similar techniques and it is found that the proposed technique is simple and gives better PSNR values than others.

Steganography is an art of activity the data within the cover like the simplest way that, it's like straightforward cover, though its hidden information. In this paper, secret image is activity into two completely different domains like as IWT (Integer Wavelet Transform) and DWT (Discrete Wavelet Transform). The cover image and secret image coefficient values square measure embedded by 512*512 exploitation fusion process techniques. We tend to applied numerous combinations of DWT and IWT on each images and obtained a decent quality stego images. The each domain offers safer with secret key and sure robustness of our algorithm. This dual wavelet transform utilized in Color Image Steganography Method (DWTSM) in Blue Channel (B-Channel) provides high capability and security. This proposed algorithm is tested with image quality parameters and compared to other algorithms. The experimental Results show that dual based approach achieved high capability and high security of our system in B-Channel and conjointly improves the performance of steganography system. The proposed algorithm achieved high PSNR quantitative relation 45 to 55 and other parameter values achieved the best solution and this methodology compared to different combination of transform.

In reversible image secret sharing (RISS), the cover image can be recovered to some degree, and a share can be comprehensible rather than noise-like. Reversible cover images play an important role in law enforcement and medical diagnosis. The comprehensible share can not only reduce the suspicion of attackers but also improve the management efficiency of shares. In this paper, we first provide a formal definition of RISS. Then, we propose an RISS algorithm for a $(k,n)$ -threshold based on the principle of the Chinese remainder theorem-based ISS (CRTISS). In the proposed RISS, the secret image is losslessly decoded by a modular operation, and the original cover image is recovered by a binarization operation, both of which are just simple operations. Theoretical analyses and experiments are provided to validate the proposed definition and algorithm.

In this study, the StegoCrypt technique is proposed using a combination of Discrete Wavelet Transform (DWT) and One-Time Pad (OTP). Cover image with size 512 ∗ 512 transformed with Wavelet transformation of four levels. For the first level to the third level, the subband LL is selected to obtain LL3 subband. At fourth level, the LL3 is then transformed into HH4 subband with help of wavelet transformation. This is done to gain strength and imperceptibility to the stego image. The secret message that is used is a binary image with a size of 32 ∗ 32. Secret image is encrypted with OTP before it is inserted in the host image. To test the quality of imperceptibility, stego images were measured using PSNR and MSE. While the quality of secret image reconstruction of extraction and decryption results are measured by using NCC. Robustness of stego image is also tested with JPEG compression attacks. From the test results can be concluded that the proposed method works well and get a better quality stego image. The secret image reconstruction results are also perfect as well as robust to JPEG compression attacks.

Image steganography performance analysis using discrete wavelet transform and alpha blending for secure communication

AFcIHNet: Attention feature-constrained network for single image information hiding

This note deals with the problem of solving the generalized Sylvester matrix equation AV-EVF=BW, with F being an arbitrary matrix, and provides complete general parametric expressions for the matrices V and W satisfying this equation. The primary feature of this approach is that the matrix F is firstly transformed into triangular form by Schur decomposition and then unimodular transformation or singular value decomposition are employed. The results can be easily extended to second order case and high order case and can provide great convenience to the computation and analysis of the solutions to this class of equations, and can perform important functions in many analysis and design problems in control systems theory.

This paper presents a novel feature extraction framework for content-based image retrieval (CBIR). Discrete wavelet transform (DWT) based Local tetra pattern (LTrP) is used to obtain the feature map from an input image. Decomposition of DWT up to single level and the features obtained from it would make the CBIR system very sensitive to noise. Therefore, decomposition up to three scales is used to remove noise. On each of the sub band LTrP is applied and 130 features are extracted. Further, Artificial Neural Network (ANN) is employed for index matching and image retrieval task which gives the classification accuracy of 97.9 % for Corel 1K database. We have compared our proposed feature extraction scheme with LTrP and other existing local descriptor. Results show that combination of DWT and Local Tetra Patterns (DWT + LTrP) extracts more robust features than LTrP alone. Also, the effect of different wavelet filters on the accuracy of the system has been analyzed. Proposed framework has been tested on Corel-1000 and Corel-10000 databases. We have used average retrieval rate, precision, and recall for performance measure of the CBIR system. Proposed method outperforms the retrieval rate from 75.9% using LTrP to 97.9% using proposed method on Corel 1K database. Improvement in performance measures as compared to the other existing methods evidence that, the proposed spatio-frequency local descriptor is more robust for image retrieval task.

An image watermarking method using Discrete Wavelet Transform (DWT) and Genetic Algorithm (GA) is presented for applications like content authentication and copyright protection. This method is robust to various image attacks. For watermark detection/extraction, the cover image is not essential. Gray scale images of size 512 × 512 as cover image and binary images of size 64 × 64 as watermark are used in the simulation of the proposed method. Watermark embedding is done in the DWT domain. 3rd and 2nd level detail sub-band coefficients are selected for further processing. Selected coefficients are arranged in different blocks. The size of the block and the number blocks depends on the size of the watermark. One watermark bit is embedded in each block. Then, inverse DWT operation is performed to get the required watermarked image. This watermarked image is used for transmission and distribution purposes. In case of any dispute over the ownership, the hidden watermark is decoded to solve the problem. Threshold-based method is used for watermark extraction. Control parameters are identified and optimized based on GA for targeted performance in terms of PSNR and NCC. Performance comparison is done with the existing works and substantial improvement is witnessed.