Robust Perceptual Image Research Articles

Robust Perceptual Image Hashing for Screen-Shooting Attack

Robust Perceptual Image Hashing for Screen-Shooting Attack

Two Robust Perceptual Image Hashing Schemes Based on Discrete Wavelet Transform

Two Robust Perceptual Image Hashing Schemes Based on Discrete Wavelet Transform

A Novel Approach for Robust Perceptual Image Hashing

Perceptual image hashing system generates a short signature called perceptual hash attached to an image before transmission and acts as side information for analyzing the trustworthiness of the received image. In this paper, we propose a novel approach to improve robustness for perceptual image hashing scheme for generating a perceptual hash that should be resistant to content-preserving manipulations, such as JPEG compression and Additive white Gaussian noise (AWGN) also should differentiate the maliciously tampered image and its original version. Our algorithm first constructs a robust image, derived from the original input by analyzing the stability of the extracted features and improving their robustness. From the robust image, which does perceptually resemble the original input, we further extract the final robust features. Next, robust features are suitably quantized allowing the generation of the final perceptual hash using the cryptographic hash function SHA1. The main idea of this paper is to transform the original image into a more robust one that allows the extraction of robust features. Generation of the robust image turns out be quite important since it introduces further robustness to the perceptual image hashing system. The paper can be seen as an attempt to propose a general methodology for more robust perceptual image hashing. The experimental results presented in this paper reveal that the proposed scheme offers good robustness against JPEG compression and Additive white Gaussian noise.

Robust Perceptual Image Hashing using SIFT and SVD

With the advancement in technology, digital data such as image, video, etc. can be easily manipulated. Image hashing is a method that can be used for authentication and identification of digital images. In this communication, a robust image hashing technique is proposed using scale invariant feature transform (SIFT), singular value decomposition (SVD) and Zernike moment. Zernike moment is used to restore the image against the rotation attack. Potential points are generated from the image by using SIFT. Block processing of equal size is performed on the input image. Key points within the same block are used to generate hashing values. The experimental outcome shows that the proposed technique can withstand different types of attacks. Receiver operating characteristic curve comparison specifies that our method outperforms other existing methods under consideration.

Blind Image Quality Assessment Using Local Consistency Aware Retriever and Uncertainty Aware Evaluator

Blind image quality assessment (BIQA) aims to automatically predict the perceptual quality of a digital image without accessing its pristine reference. Previous studies mainly focus on extracting various quality-relevant image features. By contrast, the explorations on highly efficient learning model are still very limited. Motivated by the fact that it is difficult to approximate a complex and large data set via a global parametric model, we propose a novel local learning method for BIQA to improve quality prediction performance. More specifically, we search for the perceptually similar neighbors of a test image to serve as its unique training set. Unlike the widely used ${k}$ nearest neighbors principle, which only measures the similarity between the testing and training samples, the local consistency of the selected training data is also considered to generate smoother sample space. The image quality is estimated via a sparse Gaussian process. As an additional benefit, the uncertainty of the predicted score is jointly inferred, which can subsequently drive more robust perceptual image processing applications, such as deblocking investigated in this paper. Extensive experiments demonstrate that the proposed learning model leads to consistent quality prediction improvements over many state-of-the-art BIQA algorithms.

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.