Perceptual image hash has been widely investigated in an attempt to solve the problems of image content authentication and content-based image retrieval. In this paper, we combine statistical analysis methods and visual perception theory to develop a real perceptual image hash method for content authentication. To achieve real perceptual robustness and perceptual sensitivity, the proposed method uses Watson’s visual model to extract visually sensitive features that play an important role in the process of humans perceiving image content. We then generate robust perceptual hash code by combining image-block-based features and key-point-based features. The proposed method achieves a tradeoff between perceptual robustness to tolerate content-preserving manipulations and a wide range of geometric distortions and perceptual sensitivity to detect malicious tampering. Furthermore, it has the functionality to detect compromised image regions. Compared with state-of-the-art schemes, the proposed method obtains a better comprehensive performance in content-based image tampering detection and localization.

A Visual Model-Based Perceptual Image Hash for Content Authentication

Perceptual image hashing finds increasing attention in several multimedia security applications such as image identification/authentication, tamper detection, and watermarking. Robust feature extraction is the main challenge in hashing schemes. Local binary pattern (LBP) is a new feature which is due to its simplicity, discriminative power, computational efficiency, and robustness to illumination changes has been used in various image applications. In this paper, we propose a robust image hashing scheme using center-symmetric local binary patterns (CSLBP). In the proposed image hashing, CSLBP features are extracted from each non-overlapping block within the original gray-scale image. For each block, the final hash code is obtained by inner product of its CSLBP feature vector and a pseudorandom weight vector. Furthermore, singular value decomposition (SVD) is combined with CSLBP to introduce a more robust hashing method called SVD-CSLBP. Performances of the proposed hashing schemes are evaluated with two groups of popular applications in perceptual image hashing schemes: image identification and image authentication. Experimental results show that the proposed methods are robust to a wide range of distortions and attacks such as additive noise, blurring, brightness changes and JPEG compression. Moreover, the proposed methods have this capability to localize the tampering area, which is not possible in all hashing schemes.

Perceptual image hashing using center-symmetric local binary patterns

Perceptual hashing is used for multimedia content identification and authentication through perception digests based on the understanding of multimedia content. This paper presents a literature review of image hashing for image authentication in the last decade. The objective of this paper is to provide a comprehensive survey and to highlight the pros and cons of existing state-of-the-art techniques. In this article, the general structure and classifications of image hashing based tamper detection techniques with their properties are exploited. Furthermore, the evaluation datasets and different performance metrics are also discussed. The paper concludes with recommendations and good practices drawn from the reviewed techniques.

Perceptual hashing for image authentication: A survey

The major disadvantage of existing watermarking methods is their limited resistance to extensive geometric attacks. In addition, we have found that the weakness of multiple watermark embedding methods that were initially designed to resist geometric attacks is their inability to withstand the watermark-estimation attacks (WEAs), leading to reduce resistance to geometric attacks. In view of these facts, this paper proposes a robust image watermarking scheme that can withstand geometric distortions and WEAs simultaneously. Our scheme is mainly composed of three components: 1) robust mesh generation and mesh-based watermarking to resist geometric distortions; 2) construction of media hash-based content-dependent watermark to resist WEAs; and 3) a mechanism of false positive-oriented watermark detection, which can be used to determine the existence of a watermark so as to achieve a tradeoff between correct detection and false detection. Furthermore, extensive experimental results obtained using the standard benchmark (i.e., Stirmark) and WEAs, and comparisons with relevant watermarking methods confirm the excellent performance of our method in improving robustness. To our knowledge, such a thorough evaluation has not been reported in the literature before

Media Hash-dependent Image Watermarking Resilient Against Both Geometric Attacks and Estimation Attacks Based on False Positive-Oriented Detection

The perceptual image hashing is an emerging technique which can be used in image authentication and content-based image retrieval. Recently, several image hashing schemes based on Radon transform are proposed for image authentication and retrieval. These schemes have no random information to strengthen the security with image hashing. In this paper, we propose a simple key-based secure image hashing scheme by using radon transform and 1-D Discrete Cosine Transform (DCT). Particularly, we introduce a randomization on Radon transform and make the image hash more secure and more robust. Moreover, the discriminative capability is also confirmed in our experimental results.

A key-dependent secure image hashing scheme by using Radon transform

Image hash function based on the image content has applications in watermarking, authentication and image retrieval. This paper presents an algorithm for generating an image hash that is robust against content-preserving modifications and at the same time, is capable of detecting malicious tampering. Robust features are first extracted from the discrete wavelet transform followed by the Radon transform. Probabilistic quantization is then used to map the feature values to a binary sequence. Results show that the proposed method can resist perceptually insignificant modifications such as compression, filtering, scaling and rotation. It is also able to successfully detect content changing attacks such as insertion of foreign objects.

Xin C. Guo

Papers

Content based image hashing via wavelet and radon transform