Content-preserving Operations Research Articles

Robust image hashing based on multi-view dimension reduction

In the field of multimedia security, image hashing is an effective technology for solving image content identification. Image hashing algorithm aims to map the high dimensional input features onto perceptual hashing values in terms of their perceptual content, in which visually identical images are mapped to similar digital representations. In this paper, we focus on multi-view dimensionality reduction based hashing generation, and propose a novel method, called Multi-view Dimensionality Reduction based Robust Image Hashing (MDRIH). Our MDRIH scheme maps the multi-view features into a compact binary space by considering the complementarity and similarity between image features across these different views. For preprocessing, bilinear interpolation is applied on the host image to adjust to a standard size, and ensure the different images with same hash size. Specifically, in order to improve the robustness of the algorithm against content-preserving operations, multi-view features, including structural features, edge features, and color features are extracted. After that, multi-view dimension reduction is adopted to obtain meaningful low-dimensional information from multi-feature fusion. Finally, the data in low-dimensional subspace is encoded into a compact binary sequence. The experimental results indicate that the proposed hashing method not only preserves the correlation between multiple views and the similarity among different data points within each view, but also achieves a better balance between robustness and discriminability compared to existing hashing methods.

Perceptual Image Hashing With Locality Preserving Projection for Copy Detection

Perceptual image hashing is an effective and efficient way to identify images in large-scale databases, where two major performances are robustness and discrimination. A better tradeoff between robustness and discrimination is still a severe challenge for the current hashing research. Aiming at this issue, we design a novel perceptual image Hashing with Locality Preserving Projection (LPP) (hereafter HLPP). Specifically, to improve the robustness against content-preserving operations, Gabor filtering is leveraged to adaptively extract the orientation and structure features, which are consistent with the response of human visual system. The LPP is adopted to learn intrinsic local structure from the maximum Gabor filtering response. The use of LPP can discover meaningful low-dimensional information hidden in the maximum Gabor filtering response and thus improves discrimination of HLPP. During hash similarity calculation, the Hamming distance is selected as the metric. The tradeoff performance between robustness and discrimination is validated on benchmark databases, and the results indicate that the proposed HLPP is superior to some state-of-the-art algorithms. In addition, extensive experiments of copy detection also demonstrate that the proposed HLPP can provide higher accuracy than the compared algorithms.

A Secure and Robust Autoencoder-Based Perceptual Image Hashing for Image Authentication

With the advancement of technology, it has become easier to modify or tamper with digital data effortlessly. In recent times, the image hashing algorithm has gained popularity for image authentication applications. In this paper, a convolutional stacked denoising autoencoder (CSDAE) is utilized for producing hash codes that are robust against different content preserving operations (CPOs). The CSDAE algorithm comprises mapping high-dimensional input data into hash codes while maintaining their semantic similarities. This implies that the images having similar content should have similar hash codes. To demonstrate the effectiveness of the model, the correlation between hash codes of semantically similar images has been evaluated. Subsequently, tampered localization is done by comparing the decoder output of the manipulated image with the hash of the actual image. Then, the localization ability of the model was measured by computing the f 1 scores between the predicted region and the original tampered region. Based on the comparative performance and receiver-operating characteristics (ROC) curve, we may conclude that the proposed hashing proposed algorithm provides improved performance compared to various state-of-the-art techniques.

Robust and reversible image watermarking in homomorphic encrypted domain

This paper proposes a robust and reversible watermarking scheme for the encrypted image by using Paillier cryptosystem. In the proposed method, the original image is divided into a number of non-overlapping blocks sized by 8 × 8 and Paillier cryptosystem is applied to encrypt the pixels in each block. Firstly, a data hider can calculate the statistical values of encrypted blocks by employing modular multiplicative inverse (MMI) method and looking for a mapping table. Then a watermark sequence can be embedded into the encrypted image by shifting the histogram of the statistical values. On the receiver side, the shifted histogram can be obtained from both the encrypted image and the decrypted image. Furthermore, the embedded watermark can be extracted from the shifted histogram. The encrypted original image can be restored by employing inverse operations of histogram shifting. This is followed by a decryption operation to restore the original image. In the proposed method, the hidden bits can still be extracted correctly under some typical content-preserving operations, such as JPEG/JPEG2000 compression and additive Gaussian noise. Compared with the previous reversible watermarking methods in plaintext domain, the proposed method has satisfactory performance in image quality and robustness. Experimental results have shown the validity of the proposed method.

Robust color image hashing using convolutional stacked denoising auto-encoders for image authentication

Image authentication based on image hashing has gained large attention in recent years. However, limited work has been done in color image hashing. Also, most of the existing methods are unable to detect tampering, if the composite rotation–scaling–translation (RST) distortion and tampering in a color image occur simultaneously. In this paper, an image hashing technique has been proposed based on convolutional stacked denoising auto-encoders (CSDAEs). In addition, a blind geometric correction approach is used to correct the composite RST distortion in the image. An input image is hierarchically mapped to a lower-dimensional hash code via CSDAEs, which have been trained for content-preserving operations (CPOs). An image map is generated from the hash via the decoder. The tampered area has been localized, by comparing the image map of hash codes from the reference image and the received image. The experimental results show that the proposed method is robust against most of the CPOs, especially to composite RST, a better trade-off between robustness and discrimination, and can localize the tampered regions. The receiver operating characteristics show that the proposed model is better than some of the state-of-the-art methods.

Perceptual Image Hashing Based on Three-Dimensional Global Features and Image Energy

In order to improve classification performance and operating efficiency of the hash algorithm, this paper proposes a novel hash algorithm that combines three-dimensional global features and local energy features. During the stage of three-dimensional features extraction, the image is firstly compressed by SVD decomposition to form a secondary image. Then the statistical features of the secondary image at the three-dimensional visual angle are extracted as the global features. Finally, the global feature hash is generated by using the relationship between the statistical features of the image layers from different three-dimensional visual angles. In the energy feature extraction stage, the luminance image is divided into blocks, and then the energy value of each image sub-block is obtained. The multi-directional energy change features are taken as the local features of the image. Subsequent experimental results prove the effectiveness of the proposed algorithm. The algorithm not only has good robustness to the conventional content-preserving operations, but also achieves a good balance between discrimination capability and robustness. In addition, compared with several state-of-the-art schemes, this algorithm has the best ROC curve, the shortest running time and the best local tamper detection ability.

Robust Hashing Based on Quaternion Gyrator Transform for Image Authentication

Image hashing is one of the most effective methods in many image processing applications including image recognition or authentication, tampering detection, image retrieval, etc. In this paper, we propose a novel image hash method based on quaternion gyrator transform, which is more secure and compact in addition to its robustness and discriminative properties. In this proposed method, the quaternion gyrator transform (QGT), like the traditional quaternion Fourier transform which is a linear regular integral ratio transform is applied to effectively extract image features. Firstly, our hash function needs to scale all pictures to a fixed size. And the quaternion gyrator transform is used to transform each non-overlapping block in the original image for extracting the feature map of the source image. For each block, the inner product of the feature and a random weight is used to be one bit of the final image hash. Experiments with different image databases are conducted to test and verify the efficiency of our method. The result has proved that our method of constructing hash is robust to most of the content-preserving operations with a good distinction. Compared with some state-of-the-art hash algorithms, our algorithm is more excellent and has better performance in robustness and distinguishability.

Perceptual image hashing based on frequency dominant neighborhood structure applied to Tor domains recognition

Tor (The Onion Router) is one of the most famous anonymous networks in the Deep Web. It provides a wide range of legal and illegal hidden services to the user. Recognizing such illicit domains is a challenging task for Cyber Security and Law Enforcement Agencies. However, doing it manually, based only on the officers’ experience, is slow and prone to errors. Therefore, in this paper, we propose an automatic method based on perceptual hashing to recognize services on the Tor network only by means of their snapshots. Firstly, we introduce and make publicly available DUSI-2K (Darknet Usage Service Images-2K), an image dataset which contains snapshots from active Tor service domains. We also present a new, efficient, robust and discriminative image hashing method, named F-DNS, built by incorporating the Dominant Neighborhood Structure (DNS) map and the Global Neighborhood Structure (GNS) texture energy map extracted from the discrete cosine transform of the image. In order to evaluate the efficiency of our hashing method, we carry out intra- and inter- tests using images from some state-of-the-art datasets subject to various content-preserving operations. The high correlation coefficient values that our method obtains, indicates that F-DNS performs better than other state-of-the-art methods, especially in the case of rotation. Additionally, we assess F-DNS for recognizing the category of Tor domains based on their snapshots using the DUSI-2K dataset. We compare its performance with three typical image classification methods, i.e. Bag of Visual Words (BoVW) and features obtained from ResNet50 and Inception-ResNet-v2. F-DNS outperforms all of them, with an accuracy of 98.75%, against 31.39%, 82.70% and 85.19%, respectively. Fine-tuning ResNet50 and Inception-Resnet-v2 for DUSI-2K does not improve the result, attaining 37.12% and 79.15%, respectively.

A watermarking scheme based on rotating vector for image content authentication

Semi-fragile watermarking technique for digital image, as a technology for content authentication, aims at telling malicious tampering from content-preserving operators. However, with the resolution of imaging sensors increasing and the explosive growth of digital images on the internet, before authorization, watermarked images tend to undergo such double-compression environments as: first compression (JPEG/JPEG2000) for release, decoding, application processing (conventional signal processing such as nosing, filtering, cropping, and scaling/security attack/malicious tampering), and second compression (JPEG2000/JPEG) for release again. In this paper, based on rotating vector, we propose a novel watermarking expression method that can describe carrier semantics to a certain extent and its modulation algorithm for digital image, and analyze the stability of the watermarked data theoretically. Then, a semi-fragile watermarking scheme is proposed for image content authentication to effectively distinguish malicious content manipulation from content-preserving operations in the double-compression application environment, which expands the application scope of content authentication based on watermarking technology.

Perceptual video hashing based on temporal wavelet transform and random projections with application to indexing and retrieval of near-identical videos

A perceptual video hash function extracts a short fixed-length bit string called a perceptual hash on the basis of the visual contents of the video. Such a function should be robust to the content-preserving operations and at the same time, sensitive to the content differences. In this work, the discrete wavelet transform (DWT) along the temporal direction, referred to as the temporal wavelet transform (TWT), is used for generating the temporally informative representative images (TIRIs). The resultant low pass data are projected onto the Achlioptas’s random basis to generate the hash. The TWT and the random projection technique not only reduce the dimensions but also retains the important features. Simulation results show that the proposed algorithm performs better for both the content-preserving and the content changing attacks when compared to that of the existing video hashing algorithms with the added advantage of computational efficiency. The proposed algorithm is applied to the indexing and retrieval of near-identical video application and the performance is evaluated using average precision-recall curves.

Perceptual image hashing based on a deep convolution neural network for content authentication

Image hash functions have wide-ranging application in many fields. This study presents a perceptual image-hashing scheme for a deep convolutional neural network (DCNN) for the purpose of content authentication. First, an AlexNet model of DCNN is constructed and trained to assess the performance of a given network. Then, the trained network is used to extract an image feature matrix. Finally, an image-hashing series is generated for content authentication. Experimental results show that, compared with other methods, the proposed method has a higher discrimination capability and an acceptable robustness against content-preserving operations, such as random attacks, rotation, JPEG compression, and additive Gaussian noise. A receiver operating characteristics curve is employed and demonstrates that the proposed image hashing obtains a desirable compromise between discrimination and robustness.

Image authentication based on robust image hashing with geometric correction

Image authentication based on robust image hashing has been widely discussed with continuous improvements. However, most of the existing methods misjudge the images processed through the geometric transformation and small malicious operations. In this paper, we have proposed a geometric correction approach, which eliminates the influence of geometric transformation, including composite rotation-scaling-translation (RST). We have incorporated the local features with global features to construct hash. The local features are extracted from the salient regions, which have been obtained using Markov absorption probabilities. The global features include statistical feature distance. While being robust to content-preserving operations (including the composite RST) the hash is sensitive to the small malicious operations, and hence may be used for image authentication. The proposed image authentication is a two-phase system. First, the threshold, δ, based on only global feature segregates between “different (also includes large area tampering) images” and “similar images or small tampered images”. In the next phase, the similar image pairs are authenticated and the tampered regions are localized, based on local features. The receiver operating characteristics shows that the proposed image authentication system outperforms some state-of-the-art methods.

Robust image hashing using SIFT feature points and DWT approximation coefficients

Abstract This study proposes a robust hashing method using scale-invariant feature transform (SIFT) features points and discrete wavelet transform (DWT) approximation coefficients for image authentication. Initially, the invariant feature points are computed using SIFT from the L ∗ component of L ∗ a ∗ b ∗ color image. Next, n distinct SIFT feature points are utilized to extract image content from the L ∗ component. Then, DWT is applied to extracted content in order to compute approximation coefficients. Finally, the approximation coefficients are normalized to form a binary hash. Experimental results show that the proposed method is robust to various content-preserving operations such as compression, scaling, filtering, additive noise, brightness, and contrast adjustment. In addition, the performance of the proposed method is compared to existing methods using a receiver operating characteristics curve. The comparison results show that the proposed method performs better than the existing methods.

Multi-granularity geometrically robust video hashing for tampering detection

The wide-spread video editing tools make it much easier to tamper a video, which raises a huge need for authentication techniques that can prove the originality of video content and locate the tampered regions on the video sequences. In this paper, a multi-granularity geometrically robust video hashing method is proposed for tampering detection and localization. In order to balance the robustness and sensitiveness, we describe a video from three levels of granularity: frame sequence level, block level and pixel level, and then hashes are generated at these three levels. Polar Complex Exponential Transform (PCET) moments are calculated on the low-pass sub-band of 3D Discrete Wavelet Transform (3D–DWT) on frame sequence to extract geometric invariant spatio-temporal hash, which is used for video authentication. Local PCET moments are calculated on annular and angular blocks, which are used for geometric correction and coarse tampering localization. Position information of salient objects is obtained from saliency map for fine tampering localization. Experimental results show that the proposed method is robust against temporal de-synchronization and geometrical transformation, and has high tampering localization accuracy even when the video is rotated. Compared with state-of-the-art methods, it is more robust against content-preserving operations and more sensitive to malicious manipulations.

Robust image hashing with multidimensional scaling

Multidimensional scaling (MDS) is an efficient technique for data analysis, and has been successfully applied in data visualization, object retrieval, data clustering, and so on. However, its use in image hashing is rarely discussed yet. In this study, we investigate the use of MDS in image hashing and propose an MDS-based hashing algorithm resistant to any-angle rotation. The proposed algorithm extracts a rotation-invariant feature matrix with log-polar transform and discrete Fourier transform from the normalized image, and learns a compact and discriminative representation from the feature matrix by MDS. Experiments with 3845 images are carried out and the results demonstrate that the proposed algorithm is robust to many content-preserving operations, including any-angle rotation, and reaches good discrimination. Receiver operating characteristics (ROC) curve comparisons illustrate that the proposed algorithm outperforms some state-of-the-art algorithms in classification with respect to robustness and discrimination.

Robust image hashing using ring partition-PGNMF and local features.

BackgroundImage authentication is one of the challenging research areas in the multimedia technology due to the availability of image editing tools. Image hash may be used for image authentication which should be invariant to perceptually similar image and sensitive to content changes. The challenging issue in image hashing is to design a system which simultaneously provides rotation robustness, desirable discrimination, sensitivity and localization of forged area with minimum hash length.MethodsIn this paper, a perceptually robust image hashing technique based on global and local features has been proposed. The Global feature was extracted using ring partition and projected gradient nonnegative matrix factorization (PGNMF). The ring partitioning technique converts a square image into a secondary image that makes the system rotation invariant. The PGNMF which is usually faster than the other NMFs has been used to reduce the dimension of the secondary image to generate the shorter hash sequence. The local features extracted from the salient regions of the image help to localize the forged region in the maliciously manipulated images. The image hashing techniques that use only global features are limited in discrimination.ResultsThe experimental results reveal that the proposed image hashing method based on global and local features provides better discrimination capability. The proposed hashing method is tested on large image sets collected from the different standard database. It is observed from the experimental results that the proposed system is robust to content-preserving operations and is capable of localizing the counterfeit area.ConclusionsThe combination of global and local features is robust against the content-preserving operations, which has a desirable discriminative capability. The proposed system may be used in image authentication, forensic evidence, and image retrieval, etc.

Robust image hashing with embedding vector variance of LLE

Locally linear embedding (LLE) has been widely used in data processing, such as data clustering, video identification and face recognition, but its application in image hashing is still limited. In this work, we investigate the use of LLE in image hashing and find that embedding vector variances of LLE are approximately linearly changed by content-preserving operations. Based on this observation, we propose a novel LLE-based image hashing. Specifically, an input image is firstly mapped to a normalized matrix by bilinear interpolation, color space conversion, block mean extraction, and Gaussian low-pass filtering. The normalized matrix is then exploited to construct a secondary image. Finally, LLE is applied to the secondary image and the embedding vector variances of LLE are used to form image hash. Hash similarity is determined by correlation coefficient. Many experiments are conducted to validate our efficiency and the results illustrate that our hashing is robust to content-preserving operations and reaches a good discrimination. Comparisons of receiver operating characteristics (ROC) curve indicate that our hashing outperforms some notable hashing algorithms in classification between robustness and discrimination.

Dither modulation of significant amplitude difference for wavelet based robust watermarking

This paper proposed a wavelet quantization based method for robust watermarking with resistance to incidental distortions. For transform domain based watermarking algorithms, blindly localizing adequate significant coefficients is one critical issue to guarantee the robustness while preserving good fidelity. In the proposed method, low frequency wavelet coefficients of the host image are randomly permutated into sub-groups according to a watermarking secret key. Embedding modifications are then distributed to important coefficients which preserve large perceptual capacity by quantizing the significant amplitude difference (SAD). Meanwhile, dither modulation strategy is employed to control the quantization artifacts and increase the robustness. In such a framework, the blind watermark extraction can be straightforwardly achieved with the watermarking secret keys which only shared by the embedder and extractor for advanced security. Numerous comparison experiments are conducted to evaluate the watermarking performance. Experimental results demonstrate the superiority of our scheme on robustness against content-preserving operations and incidental distortions such as JPEG compression, Gaussian noise.

Robust hashing for image authentication using quaternion discrete Fourier transform and log-polar transform

In this work, a novel robust image hashing scheme for image authentication is proposed based on the combination of the quaternion discrete Fourier transform (QDFT) with the log-polar transform. QDFT offers a sound way to jointly deal with the three channels of color images. The key features of the present method rely on (i) the computation of a secondary image using a log-polar transform; and (ii) the extraction from this image of low frequency QDFT coefficients' magnitude. The final image hash is generated according to the correlation of these magnitude coefficients and is scrambled by a secret key to enhance the system security. Experiments were conducted in order to analyze and identify the most appropriate parameter values of the proposed method and also to compare its performance to some reference methods in terms of receiver operating characteristics curves. The results show that the proposed scheme offers a good sensitivity to image content alterations and is robust to the common content-preserving operations, and especially to large angle rotation operations.

A Robust Watermarking Scheme Based on Maximum Wavelet Coefficient Modification and Optimal Threshold Technique

Digital watermarking has received extensive attention as a new method for copyright protection. This paper proposes a robust watermarking algorithm based on maximum wavelet coefficient modification and optimal threshold technique. The medium wavelet coefficients are randomly permutated according to a secret key and divided into subgroups. We modify the maximum wavelet in each subgroup according to the embedded watermark bits, which can effectively resist attacks. During the extraction process, an optimal threshold value calculated by iterative computation is used to extract the watermark from the watermarked image under different attacks, without using the original image or watermark. Numerous experiments are conducted to evaluate the watermarking performance. Experimental results demonstrate the superiority of our scheme on robustness against content-preserving operations and incidental distortions such as JPEG compression, Gaussian noise, median filter, resizing, cropping, and sharpening.