Zhenjun Tang

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.

Estimation of Image Rotation Angle Using Interpolation-Related Spectral Signatures With Application to Blind Detection of Image Forgery

Motivated by the image rescaling estimation method proposed by Gallagher (2nd Canadian Conf. Computer & Robot Vision, 2005: 65-72), we develop an image rotation angle estimator based on the relations between the rotation angle and the frequencies at which peaks due to interpolation occur in the spectrum of the images edge map. We then use rescaling/rotation detection and parameter estimation to detect fake objects inserted into images. When a forged image contains areas from different sources, or from another part of the same image, rescaling and/or rotation are often involved. In these geometric operations, interpolation is a necessary step. By dividing the image into blocks, detecting traces of rescaling and rotation in each block, and estimating the parameters, we can effectively reveal the forged areas in an image that have been rescaled and/or rotated. If multiple geometrical operations are involved, different processing sequences, i.e., repeated zooming, repeated rotation, rotation-zooming, or zooming-rotation, may be determined from different behaviors of the peaks due to rescaling and rotation. This may also provide a useful clue to image authentication.

Lexicographical framework for image hashing with implementation based on DCT and NMF

Image hash is a content-based compact representation of an image for applications such as image copy detection, digital watermarking, and image authentication. This paper proposes a lexicographical-structured framework to generate image hashes. The system consists of two parts: dictionary construction and maintenance, and hash generation. The dictionary is a large collection of feature vectors called words, representing characteristics of various image blocks. It is composed of a number of sub-dictionaries, and each sub-dictionary contains many features, the number of which grows as the number of training images increase. The dictionary is used to provide basic building blocks, namely, the words, to form the hash. In the hash generation, blocks of the input image are represented by features associated to the sub-dictionaries. This is achieved by using a similarity metric to find the most similar feature among the selective features of each sub-dictionary. The corresponding features are combined to produce an intermediate hash. The final hash is obtained by encoding the intermediate hash. Under the proposed framework, we have implemented a hashing scheme using discrete cosine transform (DCT) and non-negative matrix factorization (NMF). Experimental results show that the proposed scheme is resistant to normal content-preserving manipulations, and has a very low collision probability.

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.

Structural Feature-Based Image Hashing and Similarity Metric for Tampering Detection

Structural image features are exploited to construct perceptual image hashes in this work. The image is first preprocessed and divided into overlapped blocks. Correlation between each image block and a reference pattern is calculated. The intermediate hash is obtained from the correlation coefficients. These coefficients are finally mapped to the interval [0, 100], and scrambled to generate the hash sequence. A key component of the hashing method is a specially defined similarity metric to measure the “distance” between hashes. This similarity metric is sensitive to visually unacceptable alterations in small regions of the image, enabling the detection of small area tampering in the image. The hash is robust against content-preserving processing such as JPEG compression, moderate noise contamination, watermark embedding, re-scaling, brightness and contrast adjustment, and low-pass filtering. It has very low collision probability. Experiments are conducted to show performance of the proposed method. n n(This work was supported by the NSF of China (60773079, 60872116, and 60832010), the High-Tech Res. and Dev. Prog. of China (2007AA01Z477), the Innovative Res. Fdn. of Shanghai Univ. for Ph.D. Programs (shucx080148), and the Sci. Res. Fdn. of Guangxi Normal Univ. for Doctors.)

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.