Perceptual Image Hashing Research Articles

Image alignment based perceptual image hash for content authentication

Perceptual image hash is an emerging technology that is closely related to many applications such as image content authentication, image forging detection, image similarity detection, and image retrieval. In this work, we propose an image alignment based perceptual image hash method, and a hash-based image forging detection and tampering localization method. In the proposed method, we introduce an image alignment process to provide a framework for image hash method to tolerate a wide range of geometric distortions. The image hash is generated by utilizing hybrid perceptual features that are extracted from global and local Zernike moments combining with DCT-based statistical features of the image. The proposed method can detect various image forging and compromised image regions. Furthermore, it has broad-spectrum robustness, including tolerating content-preserving manipulations and geometric distortion-resilient. Compared with state-of-the-art schemes, the proposed method provides satisfactory comprehensive performances in content-based image forging detection and tampering localization.

Perceptual hashing for color image based on color opponent component and quadtree structure

In this paper, a perceptual image hashing scheme based on color opponent component (COC) and Quadtree structure is proposed. Firstly, image normalization and Gaussian low-pass filtering are applied on input image to produce a secondary image, and then COC is extracted from the secondary image. The color change information is calculated from COC as color feature and Quadtree decomposition (QD) is applied on the intensity image of secondary image to produce Quadtree structure features. Finally, color features and Quadtree structure features are concatenated and pseudo-randomly permuted to produce final hash sequence. Experimental results show that the proposed scheme is robust to common content-preserving manipulations. The receiver operating characteristics (ROC) curves show that the proposed method outperforms some state-of-the-art methods in the performances of perceptual robustness and discrimination.

Robust perceptual image hashing using fuzzy color histogram

Perceptual image hashing technique uses the appearance of the digital media object as human eye and generates a fixed size hash value. This hash value works as digital signature for the media object and it is robust against various digital manipulation done on the media object. This technique have been constantly in use in various application areas like content-based image retrieval, image authentication, digital watermarking, image copy detection, tamper detection, image indexing, etc., but it is difficult to generate a perfect perceptual image hash function due to the inverse relationship between its main properties i.e. perceptual robustness and discriminative capability. In this paper, a robust and desirable discrimination capable dual perceptual image hash functions are proposed which use fuzzy color histogram for hash generation. The fuzzy engine needs stable color representation to generate a robust fuzzy color histogram feature which is invariant to various content preserving attacks like gaussian low pass filtering, jpeg compression, etc. To satisfy this, $CIEL^{*}a^{*}b^{*}$ color space forms an good basis as it approximates the human visual system and it is also uniform and device independent color space. The robustness of the fuzzy color histogram is further increased by selecting the most significant bins using an experimentally selected tuning factor and the same is furthermore normalized to make it scale invariant. Our experimentation shows that hash generated with this feature is more stable and able to handle various content preserving attacks and performs better as compared to the latest techniques. Both the proposed systems able to maintain good balance between perceptual robustness with optimal TPR when the FPR $\simeq $ 0 is 0.8115 and 0.8264 and discrimination capability with the optimal FPR when TPR $\simeq $ 1 is 0.0618 and 0.0208 respectively.

Authenticity and copyright verification of printed images

Perceptual image hashing and digital watermarking are two of the extensively investigated techniques for content authentication and copyright verification of digital images, respectively. One of the significant challenges of perceptual image hashing is a better tradeoff between robustness and discrimination. On the other hand, a major objective of digital watermarking techniques is to provide enhanced robustness and imperceptibility. In this paper, we present extensive experimental analysis, detailed laboratory forensic investigations, and application of the wave atom transform (WAT) based perceptual image hashing and digital watermarking techniques for printed images. Authenticity and copyright of printed images are verified via image hashing and digital watermarking techniques which are resilient against print-scan process distortions. To fulfill the requirement of a good balance between print-scan robustness and discrimination capabilities, we propose a compact, hybrid image hash using lower and middle frequency scale bands in the wave atom coefficients tiling. The hybrid image hash generation strategy is also advantageous due to its computational simplicity and reduced size of the hash value. Furthermore, we propose an enhanced multipurpose scheme which can support multiple applications, such as image authentication and copyright protection, simultaneously, for both digital-only and print-scanned images. Experimental analysis demonstrates the ability of wave atom based image hashing and watermarking schemes to provide authenticity and copyright verification for printed color images.

Perceptual Image Hashing Using Latent Low-Rank Representation and Uniform LBP

Robustness and discriminability are the two most important features of perceptual image hashing (PIH) schemes. In order to achieve a good balance between perceptual robustness and discriminability, a novel PIH algorithm is proposed by combining latent low-rank representation (LLRR) and rotation invariant uniform local binary patterns (RiuLBP). LLRR is first applied on resized original images to the principal feature matrix and to the salient feature matrix, since it can automatically extract salient features from corrupted images. Following this, Riulocal bin features are extracted from each non-overlapping block of the principal feature matrix and of the salient feature matrix, respectively. All features are concatenated and scrambled to generate final binary hash code. Experimental results show that the proposed hashing algorithm is robust against many types of distortions and attacks, such as noise addition, low-pass filtering, rotation, scaling, and JPEG compression. It outperforms other local binary patterns (LBP) based image hashing schemes in terms of perceptual robustness and discriminability.

Perceptual image hash function via associative memory‐based self‐correcting

Paralleling with the revolutionary development of the Internet, there has been increasing concern about the copyright infringement of digital media. A central problem in copyright protection is to accurately and efficiently identify the illegal copies of copyrighted contents. Perceptual hash function, which summarises the perceptual characteristics of digital media to a short digest, is a low-cost solution to this problem. Owing to the easy-to-manipulate nature of digital media, a major challenge in designing perceptual hash function is to achieve the robustness against distortion. To tackle this problem, an associative memory-based hash function is introduced. The proposed work repairs the distortions on local image structures via associative memory-based de-noising, in the hope of simulating the self-correcting mechanism in human memory. The shape invariant descriptors of de-noised structures are then encoded to binary bits. Experimental results confirm that the proposed work outperforms representative algorithms, and it can achieve an equal error rate of 3.30 × 10 − 3 in content identification with only 80 bits.

A Novel Perceptual Hash Algorithm for Multispectral Image Authentication

The perceptual hash algorithm is a technique to authenticate the integrity of images. While a few scholars have worked on mono-spectral image perceptual hashing, there is limited research on multispectral image perceptual hashing. In this paper, we propose a perceptual hash algorithm for the content authentication of a multispectral remote sensing image based on the synthetic characteristics of each band: firstly, the multispectral remote sensing image is preprocessed with band clustering and grid partition; secondly, the edge feature of the band subsets is extracted by band fusion-based edge feature extraction; thirdly, the perceptual feature of the same region of the band subsets is compressed and normalized to generate the perceptual hash value. The authentication procedure is achieved via the normalized Hamming distance between the perceptual hash value of the recomputed perceptual hash value and the original hash value. The experiments indicated that our proposed algorithm is robust compared to content-preserved operations and it efficiently authenticates the integrity of multispectral remote sensing images.

Robust Hashing Based on Quaternion Zernike Moments for Image Authentication

The reliability and security of multimedia contents in transmission, communications, storage, and usage have attracted special attention. Robust image hashing, also referred to as perceptual image hashing, is widely applied in multimedia authentication and forensics, image retrieval, image indexing, and digital image watermarking. In this work, a novel robust image hashing method based on quaternion Zernike moments (QZMs) is proposed. QZMs offer a sound way to jointly deal with the three channels of color images without discarding chrominance information; the generated hash is thus shorter than the hash of three channels separately processing. The proposed approach's performance was evaluated on the color images database of UCID and compared with several recent and efficient methods. These experiments show that the proposed scheme provides a short hash in length that is robust to most common image content-preserving manipulations like JPEG compression, filtering, noise, scaling, and large angle rotation operations.

A Novel Perceptual Hashing for Color Images Using a Full Quaternion Representation

Quaternions have been commonly employed in color image processing, but when the existing pure quaternion representation for color images is used in perceptual hashing, it would degrade the robustness performance since it is sensitive to image manipulations. To improve the robustness in color image perceptual hashing, in this paper a full quaternion representation for color images is proposed by introducing the local image luminance variances. Based on this new representation, a novel Full Quaternion Discrete Cosine Transform (FQDCT)-based hashing is proposed, in which the Quaternion Discrete Cosine Transform (QDCT) is applied to the pseudo-randomly selected regions of the novel full quaternion image to construct two feature matrices. A new hash value in binary is generated from these two matrices. Our experimental results have validated the robustness improvement brought by the proposed full quaternion representation and demonstrated that better performance can be achieved in the proposed FQDCT-based hashing than that in other notable quaternion-based hashing schemes in terms of robustness and discriminability.

A Visual Model-Based Perceptual Image Hash for Content Authentication

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.

Image authentication using LBP-based perceptual image hashing

Feature extraction is a main step in all perceptual image hashing schemes in which robust features will led to better results in perceptual robustness. Simplicity, discriminative power, computational efficiency and robustness to illumination changes are counted as distinguished properties of Local Binary Pattern features. In this paper, we investigate the use of local binary patterns for perceptual image hashing. In feature extraction, we propose to use both sign and magnitude information of local differences. So, the algorithm utilizes a combination of gradient-based and LBP-based descriptors for feature extraction. To provide security needs, two secret keys are incorporated in feature extraction and hash generation steps. Performance of the proposed hashing method is evaluated with an important application in perceptual image hashing scheme: image authentication. Experiments are conducted to show that the present method has acceptable robustness against perceptual content-preserving manipulations. Moreover, the proposed method has this capability to localize the tampering area, which is not possible in all hashing schemes.

Security and privacy protocols for perceptual image hashing

Perceptual hashing plays an increasingly important role in digital forensics, pirate detection and source tracking of the massive networking information. Security and privacy problems exist in existing digital image hashing efforts and have negatively influenced their applications. In this paper we propose and design security and privacy protocols for perceptual image hashing. Specifically we propose the concepts "commutative perceptual hashing and encryption" and "distance preserving feature encryption" to ensure that perceptual hashes in the encryption domain of digital images can be calculated and compared. We further propose three sets of protocols to provide security assurance and privacy protection for the whole process. Each set of protocols are applied on registration, authentication and identification stages of the digital image hashing application models. We improve existing perceptual hashing methods by implementing the security–enhanced protocols. Experimental results demonstrate the effectiveness of the improved perceptual hashing methods.

Robust image hashing using invariants of Tchebichef moments

The content authenticity is critical for secure transmission of multimedia information. As a promising solution, perceptual image hashing has gain great attention. In this paper, we develop a novel algorithm for generating an image hash based on invariants of radial Tchebichef moments. The idea is justified by the fact that the radial Tchebichef moments represent the image under the orthogonal kernel, which has the desirable qualities of orthogonality and robustness. The hash values are achieved by adaptive quantization of the invariants of radial Tchebichef moments, then the random Gay code is applied in the discrete–binary conversion stage to enhance the expected discriminability. Experiments are conducted to show that the proposed hashing algorithm has superior robustness and discrimination performance compared with other state-of-the-art algorithms, in terms of receiving operating characteristic (ROC) curves.

Perceptual 3D model hashing using key-dependent shape feature

With the rapid growth of three-dimensional (3D) content, perceptual 3D model hashing will become a solution for the authentication, reliability, and copy detection of 3D content and will continue to be an important aspect of multimedia security in the future. However, perceptual 3D model hashing has not been used as widely as perceptual image or video hashing. In this study, a robust and secure perceptual 3D model hashing function is developed based on a key-dependent shape feature. The main objectives of our hashing function are to exhibit robustness against content-preserved attacks and to enable blind-detection without the use of preprocessing techniques for these types of attacks. In order to achieve these objectives, our hashing projects all of the vertices to the shape coordinates of the shape spectrum descriptor and the curvedness, and then, it segments the shape coordinates into irregular cells and computes the shape features of the cells using a permutation key and a random key. A perceptual hash is generated by binarizing the shape features. Experimental results confirm that the proposed hashing scheme shows robustness against geometrical and topological attacks and provides a unique and secure hash for each model and key.

Perceptual Hashing-Based Robust Image Authentication Scheme for Wireless Multimedia Sensor Networks

Image authentication is critical for secure image transmission and storage in a wireless multimedia sensor network (WMSN). In this paper, we propose a perceptual hashing-based robust image authentication scheme, which applies the distributed processing strategy for perceptual image hashes and can provide compactness, visual fragility, perceptual robustness, and security in digital image authentication for WMSN. In the proposed scheme, first, the cluster head node generates a secure pseudorandom chaotic sequence with keys and sends it to the image capturing node, then the image capturing node uses the received chaotic sequence to divide randomly the captured image into several overlapping rectangles, after that, two gravity centers of each rectangular region block are calculated, and finally the binary distance of the two gravity centers will be calculated in each general cluster member node. The cluster head node receives the binary sequence of the distance from all of the general cluster member nodes and combines them to form the perceptual hashing sequence to be sent to the base station for image authentication purpose. Experimental results show that the proposed scheme has satisfactory authentication ability and can ensure not only the visual fragility for perceptually distinct images but also robustness for perceptually identical images via image rotation, JPEG compression, and noise burring.

Image Retrieval using Perceptual Hashing

This paper proposes a new perceptual hashing based approach for Image Retrieval. Information in form of images is increasing at a very fast rate therefore there is need for faster and effective retrieval of specific image from image repositories. Content Based Image Retrieval (CBIR) is considered most efficient to search large image collections. The proposed method uses perceptual hash function which calculates similar hash value for similar images. Finally, using an adequate distance or similarity function to compare two perceptual hash values, it can be decided whether two images are perceptually different or not. Perceptual image hash functions can be used e.g. for the identification, authentication or integrity verification of images. In this paper we also propose a hash algorithm that is robust and secure to non malicious manipulation and sensitive to the malicious tampering. Keywords - Content based Image Retrieval (CBIR), Perceptual Hash algorithm, Image Retrieval using Perceptual Hashing, Query by Image Content (QBIC).

Wave-atoms-based multipurpose scheme via perceptual image hashing and watermarking

This paper presents a novel multipurpose scheme for content-based image authentication and copyright protection using a perceptual image hashing and watermarking strategy based on a wave atom transform. The wave atom transform is expected to outperform other transforms because it gains sparser expansion and better representation for texture than other traditional transforms, such as wavelet and curvelet transforms. Images are decomposed into multiscale bands with a number of tilings using the wave atom transform. Perceptual hashes are then extracted from the features of tiling in the third scale band for the purpose of content-based authentication; simultaneously, part of the selected hashes are designed as watermarks, which are embedded into the original images for the purpose of copyright protection. The experimental results demonstrate that the proposed scheme shows great performance in content-based authentication by distinguishing the maliciously attacked images from the nonmaliciously attacked images. Moreover, watermarks extracted from the proposed scheme also achieve high robustness against common malicious and nonmalicious image-processing attacks, which provides excellent copyright protection for images.

A Robust Image Perceptual Hashing Scheme Based on Improved Normalization Algorithm

An improved image normalization algorithm is proposed firstly. Then a geometric robust image perceptual hashing scheme is proposed based on image normalization and discrete cosine transform. The original image is preprocessed using our improved image normalization method. And the selected DCT coefficients as image feature are encrypted for security. The geometric robust image hash is achieved by quantizing encrypted DCT coefficients and coding. The experimental results show that the algorithm can resist against common global affine transformations such as rotation, scaling, translation and their combinations.

Detecting Phishing Web Pages Based on Image Perceptual Hashing Technology

Detecting Phishing Web Pages Based on Image Perceptual Hashing Technology

Improvement of Radon Transform-Based Perceptual Hashing using Image Normalization

This paper proposes an improvement method using image normalization for Radon transform (RT)-based perceptual image hashing in which a normalized image is generated before the image hashing estimation doing it invariant to a wide range of geometric distortions. Proposed scheme improves the robustness of previously proposed RT-based image hashing schemes against geometric distortions. The proposed improvement is evaluated using two efficient RT-based image hashing algorithms, which are Wu’s and Seo’s algorithms. The evaluation results demonstrate that the proposed method, significantly improves the efficiency of Wu’s and Seo’s image hashing algorithms when the images to be analysed are distorted by several geometric attacks, such as rotation, scaling and shearing.