Perceptual Hashing Sequence Research Articles

Perceptual Image Hashing Based on Multitask Neural Network

With the advent of the era of multimedia and in-depth development, the whole human society has been produced and spread a huge amount of image data, but at the same time, in view of the digital image and tamper with the attack of piracy phenomenon also more and more serious, malicious attacks will produce serious social, military, and political influence, therefore, to protect the authenticity of the original image content, which is also more and more important. In order to further improve the performance of image hashing and enhance the protection of image data, we proposed an end-to-end dual-branch multitask neural network based on VGG-19 to produce a perceptual hash sequence and used prepart of network of pretrained VGG-19 model to extract image features, and then, the image features are transformed into a hash sequence through a convolutional and fully connected network. At the same time, in order to enhance the function of the network and improve the adaptability of the proposed network to using scenarios, the rest part of the network layer of the VGG-19 model was used as another branch for image classification, so as to realize the multitask characteristics of the network. Through the experiment of the testing set, the network can not only resist many kinds of attack operations (content retention operations), but also realize accurate classification about the image, and has a satisfactory tampering detection ability.

An encrypted speech authentication and tampering recovery method based on perceptual hashing

With the progress of speech retrieval technology in the cloud, it brings a lot of conveniences for speech user. Yet, the inquiry encrypted speech results from the speech retrieval system are faced with some secure issues to settle, such as integrity authentication and tampering recovery. In this paper, an encrypted speech authentication and tampering recovery method based on perceptual hashing is proposed. Firstly, the original speech is scrambled by Duffing mapping to construct an encrypted speech library in the cloud, through extracting product of uniform sub-band spectrum variance and spectral entropy of encrypted speech and constructing a perceptual hashing sequence to generate the hashing template of the cloud. From this, a one-to-one correspondence between the encrypted speech and perceptual hashing sequence is established. Secondly, the authentication digest of encrypted speech is extracted according to the inquiry result during the retrieval. Then, the authentication digest and the perceptual hashing sequence of the hashing template in the cloud are matched by the Hamming distance algorithm. Finally, for encrypted speech that fails authentication, tampering detection and location are performed, and the tampered samples are recovered by the least square curve fitting method. The simulation results show that the proposed method can extract the authentication digest directly in the encrypted speech, and the authentication digest not only has good discrimination and robustness, but it accurately locates the tampered area for malicious substitution and mute attacks. In addition, the proposed method can recover tampered speech signals in high quality without any extra information.

A Subject-Sensitive Perceptual Hash Based on MUM-Net for the Integrity Authentication of High Resolution Remote Sensing Images

Data security technology is of great significance to the application of high resolution remote sensing image (HRRS) images. As an important data security technology, perceptual hash overcomes the shortcomings of cryptographic hashing that is not robust and can achieve integrity authentication of HRRS images based on perceptual content. However, the existing perceptual hash does not take into account whether the user focuses on certain types of information of the HRRS image. In this paper, we introduce the concept of subject-sensitive perceptual hash, which can be seen as a special case of conventional perceptual hash, for the integrity authentication of HRRS image. To achieve subject-sensitive perceptual hash, we propose a new deep convolutional neural network architecture, named MUM-Net, for extracting robust features of HRRS images. MUM-Net is the core of perceptual hash algorithm, and it uses focal loss as the loss function to overcome the imbalance between the positive and negative samples in the training samples. The robust features extracted by MUM-Net are further compressed and encoded to obtain the perceptual hash sequence of HRRS image. Experiments show that our algorithm has higher tamper sensitivity to subject-related malicious tampering, and the robustness is improved by about 10% compared to the existing U-net-based algorithm; compared to other deep learning-based algorithms, this algorithm achieves a better balance between robustness and tampering sensitivity, and has better overall performance.

A retrieval algorithm of encrypted speech based on short-term cross-correlation and perceptual hashing

In order to achieve extraction perceptual features from the encryption speech as a search digest for the content-based encryption speech retrieval, we present a retrieval algorithm of encrypted speech based on short-term cross-correlation and perceptual hashing in this paper. Firstly, the study encrypts the speech file and uploads the encrypted speech data to the encryption speech database in cloud server. Secondly, the sample speech clips are obtained by the cutting operation from the speech file for scrambling encryption. The perceptual hashing sequence of the encrypted speech is constructed by extracting the short-term cross-correlation of the encrypted speech signals as the search digest. These perceptual hashing sequences are uploaded into the hashing index table of cloud server. Finally, the Hamming distance algorithm is used for the matching retrieval operation during the search. The experimental results show that the proposed algorithm of encrypted speech perceptual hashing has a better discrimination, robustness and compactness, and the perceptual hashing sequences can be extracted directly from the encrypted sample speech. Meanwhile, the encryption speech signal has high recall and precision ratios after various content preserving operations. In the whole retrieval process, the downloading and decrypting operations of speech data are not necessary.

Perceptual Hashing Based Forensics Scheme for the Integrity Authentication of High Resolution Remote Sensing Image

High resolution remote sensing (HRRS) images are widely used in many sensitive fields, and their security should be protected thoroughly. Integrity authentication is one of their major security problems, while the traditional techniques cannot fully meet the requirements. In this paper, a perceptual hashing based forensics scheme is proposed for the integrity authentication of a HRRS image. The proposed scheme firstly partitions the HRRS image into grids and adaptively pretreats the grid cells according to the entropy. Secondly, the multi-scale edge features of the grid cells are extracted by the edge chains based on the adaptive strategy. Thirdly, principal component analysis (PCA) is applied on the extracted edge feature to get robust feature, which is then normalized and encrypted with secret key set by the user to receive the perceptual hash sequence. The integrity authentication procedure is achieved via the comparison between the recomputed perceptual hash sequence and the original one. Experimental results have shown that the proposed scheme has good robustness to normal content-preserving manipulations, has good sensitivity to detect local subtle and illegal tampering of the HRRS image, and has the ability to locate the tampering area.

A high-performance speech perceptual hashing authentication algorithm based on discrete wavelet transform and measurement matrix

Aiming at the problems of existing speech authentication algorithms, such as poor robustness and discrimination, security vulnerability, low efficiency, poor ability of tamper detection and localization, a high-performance speech perceptual hashing authentication algorithm based on Discrete Wavelet Transform (DWT) and measurement matrix is proposed in this paper. Firstly, the speech signal is conducted with DWT by applying preprocessing, and the low-frequency wavelet coefficients are regarded as the perceptual feature value. Then the measurement matrix controlled by chaos map is applied to reduce the dimension of feature value. Finally, the feature value is used to generate the perceptual hash sequence by the process of hashing structure. The measurement matrix is designed as the secret key to enhance the security of the proposed algorithm. The experimental results demonstrates the proposed algorithm has high efficiency in perceptual robustness, discrimination, time consumption and security, as well as having a high accuracy of tampering detection and localization.

An efficient speech perceptual hashing authentication algorithm based on DWT and symmetric ternary string

According to the situation that speech perceptual hashing methods are not appropriated for real-time speech content authentication in mobile computing environment, a novel DWT-based perceptual hashing algorithm, which uses a combination of time-domain and frequency-domain features, was proposed to protect the speech data in the cloud. Firstly, by discrete wavelet transform (DWT), a new signal in frequency-domain is generated from the original speech signal after pre-processing and intensity-loudness transform (ILT). Secondly, coefficients of low frequency wavelet decomposition are partitioned into equal-sized and non-overlapping blocks, and logarithmic short-time energy of each block is computed to obtain speech signals features in frequency-domain. Finally, combined with spectral flux features (SFF) of speech signal in time-domain, a ternary perceptual hashing sequence is created. Experiment results illustrate that ternary form is better to stand for hash digest than binary form, the proposed algorithm has a good robustness against content preserving operations, discrimination, good compaction and high efficiency, and detects the tamper localisation as well.

Vision-Based Fingertip Tracking Utilizing Curvature Points Clustering and Hash Model Representation

Fingertip tracking plays an increasingly important role in augmented reality and virtual-reality applications. However, existing approaches (either continuous-detection-based or separated-detection-tracking-based methods) cannot effectively learn temporal-spatial information or even separate fingertip tracking into unrelated stages, which causes poor real-time performance and incomplete tracking continuity. Moreover, due to the need for high-cost devices, the high degrees of freedom of the hand, and subtle differences among fingers, fingertip tracking remains a challenging task. To address these problems, we propose a novel tracking-combined-with-detection approach for vision-based fingertip tracking. By adopting clustering and geometric constraint analysis, we develop a curvature points clustering method for fingertip detection. Then, by exploiting the identified fingertip points for motion estimation with bidirectional optical flows and temporal-spatial probability calculation, the tracking stage is effectively integrated with the detection stage. To accurately locate the fingertip, we represent the fingertip model with a perceptual hash sequence and locate the fingertip by searching for the best-matching region. Extensive experimental results show the superiority of the proposed algorithm to commonly used and state-of-the-art methods and demonstrate its effectiveness and practicability.

An efficient perceptual hashing based on improved spectral entropy for speech authentication

Combined with the linear prediction-minimum mean squared error (LP-MMSE), an efficient perceptual hashing algorithm based on improved spectral entropy for speech authentication was proposed in this paper. The linear prediction analysis is conducted on speech signal after preprocessing, framing and adding windows, and obtained the minimum mean squared error coefficient matrix. And then, the spectral entropy parameter matrix of each frame is calculated by using improved spectral entropy method. And the final binary perceptual hashing sequence is generated based on the above two matrices, and the speech authentication is completed. Comparing the experimental results of combining the Teager energy operator (TEO) with the linear predictive coefficients (LPC), LP-MMSE and line spectrum pair (LSP) coefficient respectively, it can be seen that the proposed algorithm had a good compromise between robustness, discrimination and authentication efficiency, and the proposed algorithm can meet the requirement of real-time speech authentication in speech communication. Experimental results show that the proposed algorithm was better than other existing methods in compactness.

Efficient Robust Speech Authentication Algorithm for Perceptual Hashing Based on Hilbert-Huang Transform

According to the resource-constrained of mobile communication terminal, the traditional broadband speech authentication algorithm cannot satisfy the authentication of a small amount of data, strong robustness and high e‐ciency, etc. This paper, using the short-time stable characteristics of speech signal, proposed an e‐cient robust speech authentication algorithm for perceptual hashing based on Hilbert-Huang Transform (HHT). Firstly, the algorithm obtains to the HHT coe‐cients of each speech frames and constitutes an HHT coe‐cients matrix after pre-processing, framing and adding window to speech signal. Then, the HHT coe‐cients matrix for two-dimensional Discrete Cosine Transform (DCT) generates DCT coe‐cients. It based on DCT coe‐cient characteristics to evaluate its entropy. Finally, a quantization step gets authentication perceptual hash sequence. Experimental results show that the algorithm, compared with the existing methods, has a good robustness and discrimination for content preserving operations, and the short authentication time. Also, it can meet the real-time requirement of the speech perceptual authentication.

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.