Xianquan Zhang

Robust Perceptual Image Hashing Based on Ring Partition and NMF

This paper designs an efficient image hashing with a ring partition and a nonnegative matrix factorization (NMF), which has both the rotation robustness and good discriminative capability. The key contribution is a novel construction of rotation-invariant secondary image, which is used for the first time in image hashing and helps to make image hash resistant to rotation. In addition, NMF coefficients are approximately linearly changed by content-preserving manipulations, so as to measure hash similarity with correlation coefficient. We conduct experiments for illustrating the efficiency with 346 images. Our experiments show that the proposed hashing is robust against content-preserving operations, such as image rotation, JPEG compression, watermark embedding, Gaussian low-pass filtering, gamma correction, brightness adjustment, contrast adjustment, and image scaling. Receiver operating characteristics (ROC) curve comparisons are also conducted with the state-of-the-art algorithms, and demonstrate that the proposed hashing is much better than all these algorithms in classification performances with respect to robustness and discrimination.

Secure Image Encryption without Size Limitation Using Arnold Transform and Random Strategies

Encryption is an efficient way to protect the contents of digital media. Arnold transform is a significant technique of image encryption, but has weaknesses in security and applications to images of any size. To solve these problems, we propose an image encryption scheme using Arnold transform and random strategies. It is achieved by dividing the image into random overlapping square blocks, generating random iterative numbers and random encryption order, and scrambling pixels of each block using Arnold transform. Experimental results show that the proposed encryption scheme is robust and secure. It has no size limitation, indicating the application to any size images.

Robust Image Hashing With Ring Partition and Invariant Vector Distance

Robustness and discrimination are two of the most important objectives in image hashing. We incorporate ring partition and invariant vector distance to image hashing algorithm for enhancing rotation robustness and discriminative capability. As ring partition is unrelated to image rotation, the statistical features that are extracted from image rings in perceptually uniform color space, i.e., CIE L*a*b* color space, are rotation invariant and stable. In particular, the Euclidean distance between vectors of these perceptual features is invariant to commonly used digital operations to images (e.g., JPEG compression, gamma correction, and brightness/contrast adjustment), which helps in making image hash compact and discriminative. We conduct experiments to evaluate the efficiency with 250 color images, and demonstrate that the proposed hashing algorithm is robust at commonly used digital operations to images. In addition, with the receiver operating characteristics curve, we illustrate that our hashing is much better than the existing popular hashing algorithms at robustness and discrimination.

Efficient image encryption with block shuffling and chaotic map

Image encryption is a useful technique for many applications, such as image content protection, image authentication, pay-TV and data hiding. In this paper, we propose an efficient image encryption algorithm with block shuffling and chaotic map. The proposed algorithm divides an input image into overlapping blocks, shuffles image blocks to make initial encryption, exploits a chaotic map and Arnold transform to generate secret matrices, and achieves final encryption by conducting exclusive OR operations between corresponding elements of each block and a random secret matrix. Many experiments are done to validate efficiency and advantages of the proposed algorithm.

Fast communication: Robust image hashing using ring-based entropies

Image hashing is an emerging technology in multimedia security for applications such as image authentication, digital watermarking, and image copy detection. In this paper, we propose a robust image hashing based on the observations that image pixels of each ring are almost unchanged after rotation and ring-based image entropies are approximately linearly changed by content-preserving operations. This hashing is achieved by converting the input image into a normalized image, dividing the normalized image into different rings and extracting the ring-based entropies to produce hash. Hash similarity is measured by correlation coefficient. Experiments show that our hashing is robust against content-preserving manipulations such as JPEG compression, watermark embedding, scaling, rotation, brightness and contrast adjustment, gamma correction and Gaussian low-pass filtering. Receiver operating characteristics (ROC) curve comparisons with notable algorithms indicate that our hashing has better classification performances than the compared algorithms.

Robust image hashing via colour vector angles and discrete wavelet transform

Colour vector angle has been widely used in edge detection and image retrieval, but its investigation in image hashing is still limited. In this study, the authors investigate the use of colour vector angle in image hashing and propose a robust hashing algorithm combining colour vector angles with discrete wavelet transform (DWT). Specifically, the input image is firstly resized to a normalised size by bi-cubic interpolation and blurred by a Gaussian low-pass filter. Colour vector angles are then calculated and divided into non-overlapping blocks. Next, block means of colour vector angles are extracted to form a feature matrix, which is further compressed by DWT. Image hash is finally formed by those DWT coefficients in the LL sub-band. Experiments show that the proposed hashing is robust against normal digital operations, such as JPEG compression, watermarking embedding and rotation within 5°. Receiver operating characteristics curve comparisons are conducted and the results show that the proposed hashing is better than some well-known algorithms.

Perceptual image hashing using local entropies and DWT

Abstract Image hashing is an emerging technology in multimedia security. It uses a short string called image hash to represent an input image and finds applications in image authentication, tamper detection, digital watermark, image indexing, content-based image retrieval and image copy detection. This paper presents a hashing algorithm based on the observation that block entropies are approximately linearly changed after content-preserving manipulations. This is done by converting the input image to a fixed size, dividing the normalised image into non-overlapping blocks, extracting entropies of image blocks and applying a single-level 2D discrete wavelet transform to perform feature compression. Correlation coefficient is exploited to evaluate similarity between hashes. Experimental results show that the proposed algorithm is robust against content-preserving operations, such as JPEG compression, watermark embedding, Gamma correction, Gaussian low-pass filtering, adjustments of brightness and contrast, scaling and small angle rotation. Similarity values between hashes of different images are small, indicating good performances in discriminative capability.

High capacity data hiding based on interpolated image

We investigate the use of parabolic interpolation in data hiding and propose a novel data hiding algorithm with high capacity based on interpolated image. Specifically, the proposed algorithm creates an interpolated image from input image by parabolic interpolation, and embeds secret bits into interpolated pixels in terms of the relation between the interpolated value and the mean value. Ten standard benchmark images are taken as test images for validating efficiency of our algorithm. The results illustrate that our algorithm has better performances than some popular data hiding methods in embedding capacity and visual quality with respect to PSNR and SSIM.

Perceptual image hashing with histogram of color vector angles

Image hashing is an emerging technology for the need of, such as image authentication, digital watermarking, image copy detection and image indexing in multimedia processing, which derives a content-based compact representation, called image hash, from an input image. In this paper we study a robust image hashing algorithm with histogram of color vector angles. Specifically, the input image is first converted to a normalized image by interpolation and low-pass filtering. Color vector angles are then calculated. Thirdly, the histogram is extracted for those angles in the inscribed circle of the normalized image. Finally, the histogram is compressed to form a compact hash. We conduct experiments for evaluating the proposed hashing, and show that the proposed hashing is robust against normal digital operations, such as JPEG compression, watermarking embedding, scaling, rotation, brightness adjustment, contrast adjustment, gamma correction, and Gaussian low-pass filtering. Receiver operating characteristics (ROC) curve comparisons indicate that our hashing performs much better than three representative methods in classification between perceptual robustness and discriminative capability.

Image encryption based on random projection partition and chaotic system

Image encryption is a useful technique of multimedia security and has been widely used in content protection, image authentication, data hiding, and so on. In this paper, we investigate the use of projection partition in image encryption, and then design an efficient image encryption algorithm based on random projection partition and chaotic system. Specifically, our algorithm randomly divides the input image into overlapping blocks. For each block, our algorithm further divides it into a set of projection lines. And then, chaotic system is exploited to generate a secret data pool. Finally, data encryption is done by random projection line swapping and XOR operation between projection line and secret sequence selected from the secret data pool. Many experiments are conducted to validate efficiency of our algorithm. Comparisons are also done and the results show that our algorithm is better than some popular algorithms.