Robust Hashing Research Articles

Robust Hashing for Image Authentication Using Zernike Moments and Local Features

A robust hashing method is developed for detecting image forgery including removal, insertion, and replacement of objects, and abnormal color modification, and for locating the forged area. Both global and local features are used in forming the hash sequence. The global features are based on Zernike moments representing luminance and chrominance characteristics of the image as a whole. The local features include position and texture information of salient regions in the image. Secret keys are introduced in feature extraction and hash construction. While being robust against content-preserving image processing, the hash is sensitive to malicious tampering and, therefore, applicable to image authentication. The hash of a test image is compared with that of a reference image. When the hash distance is greater than a threshold τ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and less than τ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , the received image is judged as a fake. By decomposing the hashes, the type of image forgery and location of forged areas can be determined. Probability of collision between hashes of different images approaches zero. Experimental results are presented to show effectiveness of the method.

Content-Based Image Retrial Based on Hadoop

Generally, time complexity of algorithms for content-based image retrial is extremely high. In order to retrieve images on large-scale databases efficiently, a new way for retrieving based on Hadoop distributed framework is proposed. Firstly, a database of images features is built by using Speeded Up Robust Features algorithm and Locality-Sensitive Hashing and then perform the search on Hadoop platform in a parallel way specially designed. Considerable experimental results show that it is able to retrieve images based on content on large-scale cluster and image sets effectively.

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.

강인한 오디오 핑거프린팅 시스템을 위한 에너지와 통계적 필터링

디지털 음악과 스마트 폰이 대중화되면서 잡음에 강인한 실시간 음악 핑거프린트 시스템이 다양하게 개발되고 있다. 특히 핑거프린트 알고리즘 중 Multiple Hashing(MLH)은 잡음에 강인하고 정교한 구조로 되어 있다. 본 논문에서는 음악 데이터베이스로부터 질의 및 응답의 정확도를 개선하기 위해 에너지 집중필터를 사용하고 연속성과 중복성을 제거하는 통계적 필터를 제안한다. 에너지 집중 필터는 하위 비트에 에너지가 집중되는 Discrite Cosine Transform(DCT)의 특징을 이용하고, 통계적 필터는 검색된 핑거프린트 정보들 사이의 상관관계 특성을 이용한다. 실험 결과로 잡음 환경에서 에너지와 통계적 필터링으로 구성된 제안 알고리즘은 우수성을 보인다. 이는 제안된 필터 엔진으로 Philips Robust Hash(PRH)보다 잡음에 강인하고 Multiple Hashing(MLH)보다 간결한 핑거프린트 시스템을 구성할 수 있다. The popularity of digital music and smart phones led to develope noise-robust real-time audio fingerprinting system in various ways. In particular, The Multiple Hashing(MLH) of fingerprint algorithms is robust to noise and has an elaborate structure. In this paper, we propose a filter engine based on MLH to achieve better performance. In this approach, we compose a energy-intensive filter to improve the accuracy of Q/R from music database and a statistic filter to remove continuity and redundancy. The energy-intensive filter uses the Discrite Cosine Transform(DCT)'s feature gathering energy to low-order bits and the statistic filters use the correlation between searched fingerprint's information. Experimental results show that the superiority of proposed algorithm consists of the energy and statistical filtering in noise environment. It is found that the proposed filter engine achieves more robust to noise than Philips Robust Hash(PRH), and a more compact way than MLH.

Improve the security of image robust hash using fuzzy commitment scheme

The security of image robust hash has drawn great attention as an open question. A great similarity between the security requirements of the image robust digest and biometric template is found through the analysis and comparison between the vulnerability of the robust hash-based image authentication system and the biometric authentication system. A security improvement scheme of image robust hash is proposed in this paper based on fuzzy commitment scheme, which is originally a biometric template protection scheme. In our scheme, it is unnecessary to store the image robust digest in the database directly. In addition, the matching operation can be implemented in the cryptographic field. Many security weaknesses, such as the weakness induced by the poor diffusion of an image robust hash algorithm, can be overcome. The experimental results have also proved the feasibility of our scheme.

An Authentication Protocol Based on Combined RFID-Biometric System

Radio Frequency Identification (RFID) and biometric technologies saw fast evolutions during the last years and which are used in several applications, such as access control. Among important characteristics in the RFID tags, we mention the limitation of resources (memory, energy, …). Our work focuses on the design of a RFID authentication protocol which uses biometric data and which confirms the secrecy, the authentication and the privacy. Our protocol requires a PRNG (Pseud-Random Number Generator), a robust hash function and Biometric hash function. The Biometric hash function is used to optimize and to protect biometric data. For Security analysis of protocol proposed, we will use AVISPA and SPAN tools to verify the authentication and the secrecy.

Approximate Nearest Neighbor Based Feature Quantization Algorithm for Robust Hashing

Approximate Nearest Neighbor Based Feature Quantization Algorithm for Robust Hashing

Robust Image Hashing Based on Random Gabor Filtering and Dithered Lattice Vector Quantization

In this paper, we propose a robust-hash function based on random Gabor filtering and dithered lattice vector quantization (LVQ). In order to enhance the robustness against rotation manipulations, the conventional Gabor filter is adapted to be rotation invariant, and the rotation-invariant filter is randomized to facilitate secure feature extraction. Particularly, a novel dithered-LVQ-based quantization scheme is proposed for robust hashing. The dithered-LVQ-based quantization scheme is well suited for robust hashing with several desirable features, including better tradeoff between robustness and discrimination, higher randomness, and secrecy, which are validated by analytical and experimental results. The performance of the proposed hashing algorithm is evaluated over a test image database under various content-preserving manipulations. The proposed hashing algorithm shows superior robustness and discrimination performance compared with other state-of-the-art algorithms, particularly in the robustness against rotations (of large degrees).

Robust image hash in Radon transform domain for authentication

Image authentication has become an emergency issue in the digital world as it can be easily tampered with the image editing techniques. In this paper, a novel robust hashing method for image authentication is proposed. The reported scheme first performs Radon transform (RT) on the image, and calculates the moment features which are invariant to translation and scaling in the projection space. Then discrete Fourier transform (DFT) is applied on the moment features to resist rotation. Finally, the magnitude of the significant DFT coefficients is normalized and quantized as the image hash bits. Experimental results show that the proposed algorithm can tolerate almost all the typical image processing manipulations, including JPEG compression, geometric distortion, blur, addition of noise, and enhancement. Compared with other approaches in the literature, the reported method is more effective for image authentication in terms of detection performance and the hash size.

Color Image Feature Extraction for Robust Hash

Color Image Feature Extraction for Robust Hash

Secure Robust Hash Functions and Their Applications in Non-interactive Communications

A robust hash function allows different parties to extract a consistent key from a common fuzzy source, e.g., an image gone through noisy channels, which can then be used to establish a cryptographic session key among the parties without the need for interactions. These functions are useful in various communication scenarios, where the security notions are different. The authors study these different security notions in this paper and focus on forgery attacks, where the objective of the attack is to compute the extracted key (hash value) of a given message. This paper will examine information-theoretical security against forgery under chosen message attacks. The authors prove that it is not possible due to the entropy of the hash value of a given message can be reduced arbitrarily when sufficient message/hash pairs have been observed. In this regard, the authors give a computationally secure scheme, where it is computationally infeasible to compute the hash value even when its entropy may not be high.

Robust Hash for Detecting and Localizing Image Tampering

In order to improve the sensitive to illegal manipulations of image hashing, a novel robust image hashing algorithm based on fractional Fourier transform (FRFT) for detecting and localizing image tampering is proposed in this paper. The framework of generating an image hashing includes three steps: preprocessing, feature extracting and post processing. The robust hashing sequence is obtained by FRFT coefficients of image blocks. The security of proposed algorithm is totally depended on the orders of FRFT which are saved as secret keys. Experimental results show that the proposed scheme is robust against perceptually acceptable modifications to the image such as JPEG compression, mid-filtering, and rotation, more importantly the tampered place can be identified accurately.

Retrieval Oriented Robust Audio Hashing

Aiming at content-based audio retrieval (CBAR) applications, a robust audio hashing scheme is proposed. First the audio is divided to frame by fixed length and then low-frequent and high-frequent components are obtained by three-level lifting-based wavelet transformation in every frame. Secondly the audio frame is approximately represented as a product of a base matrix and an encoding matrix, or coefficient matrix, using non-negative matrix factorization (NMF). Finally the sum of each column in the coefficient matrix is calculated, which is then quantized to produce one bit of the hash sequence. Experiment results show that the proposed scheme is robust against Mp3 compression, Real compression, filtering, amplitude compression, equalization, echo, etc. It is insensitive to small local change, and therefore is suitable for distinguishing different audios.

Robust audio hashing based on discrete-wavelet-transform and non-negative matrix factorisation

Robust audio hashing defines a feature vector that characterises the audio signal, independent of content preserving manipulations, such as MP3 compression, amplitude boosting/cutting, hiss reduction, and so on. It provides a tool for fast and reliable identification of content in audio communications. In this study, the authors propose a new audio hashing based on discrete wavelet transform and non-negative matrix factorisation (NMF). The desirable property of NMF for hashing algorithm is its non-negative constraints, which result in bases that capture local feature of the audio, thereby significantly reducing misclassification. In addition, to ensure perceptual robustness, NMF is performed on the coarse wavelet coefficients, which are a low-pass approximation of the audio and not easy to change by content preserving manipulations. Experimental results over a large database reveal that the proposed scheme is more robust and provides much stronger discrimination than the conventional energy spectrum-based hashing algorithm, and that the proposed scheme can be applied in broadcast monitoring, successfully.

More Robust Hashing: Cuckoo Hashing with a Stash

Cuckoo hashing holds great potential as a high-performance hashing scheme for real applications. Up to this point, the greatest drawback of cuckoo hashing appears to be that there is a polynomially small but practically significant probability that a failure will occur during the insertion of an item, requiring an expensive rehashing of all items in the table. In this paper, we show that this failure probability can be dramatically reduced by the addition of a very small constant-sized stash. We demonstrate both analytically and through simulations that stashes of size equivalent to only three or four items yield tremendous improvements, enhancing cuckoo hashing's practical viability in both hardware and software. Our analysis naturally extends previous analyses of multiple cuckoo hashing variants, and the approach may prove useful in further related schemes.

Robust Speech Hashing for Content Authentication

Robust hashing for multimedia authentication is an emerging research area. A novel key-dependent robust speech hashing based on speech production model is proposed in this letter. Robust hash is calculated based on linear spectrum frequencies (LSFs) which model the vocal tract. The correlation between LSFs is decoupled by discrete cosine transformation (DCT). A randomization scheme controlled by a secret key is applied in hash generation for random feature selection. The hash function is key-dependent and collision resistant. Meanwhile, it is highly robust to content preserving operations as well as having high accuracy of tampering localization.

Audio Fingerprinting Based on Multiple Hashing in DCT Domain

Audio fingerprinting techniques aim at successfully performing content-based audio identification even when the audio signals are slightly or seriously distorted. In this letter, we propose a novel audio fingerprinting technique based on multiple hashing. In order to improve the robustness of hashing, multiple hash strings are generated through the discrete cosine transform (DCT) which is applied to the temporal energy sequence in each subband. Experimental results show that the proposed algorithm outperforms the Philips Robust Hash (PRH) algorithm under various distortions.

Adaptive Load Sharing for Network Processors

A novel scheme for processing packets in a router is presented that provides load sharing among multiple network processors distributed within the router. It is complemented by a feedback control mechanism designed to prevent processor overload. Incoming traffic is scheduled to multiple processors based on a deterministic mapping. The mapping formula is derived from the robust hash routing (also known as the highest random weight - HRW) scheme, introduced in K. W. Ross, IEEE Network, 11(6), 1997, and D. G. Thaler et al., IEEE Trans. Networking, 6(1), 1998. No state information on individual flow mapping has to be stored, but for each packet, a mapping function is computed over an identifier vector, a predefined set of fields in the packet. An adaptive extension to the HRW scheme is provided to cope with biased traffic patterns. We prove that our adaptation possesses the minimal disruption property with respect to the mapping and exploit that property to minimize the probability of flow reordering. Simulation results indicate that the scheme achieves significant improvements in processor utilization. A higher number of router interfaces can thus be supported with the same amount of processing power.

Distortion Estimation in Compressed Music Using Only Audio Fingerprints

An audio fingerprint is a compact yet very robust representation of the perceptually relevant parts of an audio signal. It can be used for content-based audio identification, even when the audio is severely distorted. Audio compression changes the fingerprint slightly. We show that these small fingerprint differences due to compression can be used to estimate the signal-to-noise ratio (SNR) of the compressed audio file compared to the original. This is a useful content-based distortion estimate, when the original, uncompressed audio file is unavailable. The method uses the audio fingerprints only. For stochastic signals distorted by additive noise, an analytical expression is obtained for the average fingerprint difference as function of the SNR level. This model is based on an analysis of the Philips robust hash (PRH) algorithm. We show that for uncorrelated signals, the bit error rate (BER) is approximately inversely proportional to the square root of the SNR of the signal. This model is extended to correlated signals and music. For an experimental verification of our proposed model, we divide the field of audio fingerprinting algorithms into three categories. From each category, we select an algorithm that is representative for that category. Experiments show that the behavior predicted by the stochastic model for the PRH also holds for the two other algorithms.

Markov Modelling of Fingerprinting Systems for Collision Analysis

Multimedia fingerprinting, also known as robust or perceptual hashing, aims at representing multimedia signals through compact and perceptually significant descriptors (hash values). In this paper, we examine the probability of collision of a certain general class of robust hashing systems that, in its binary alphabet version, encompasses a number of existing robust audio hashing algorithms. Our analysis relies on modelling the fingerprint (hash) symbols by means of Markov chains, which is generally realistic due to the hash synchronization properties usually required in multimedia identification. We provide theoretical expressions of performance, and show that the use of M-ary alphabets is advantageous with respect to binary alphabets. We show how these general expressions explain the performance of Philips fingerprinting, whose probability of collision had only been previously estimated through heuristics.