Table 1 - uploaded by Manesh Thankappan
Content may be subject to copyright.
Tampering Localization.
Source publication
Image hashing technique constructs a short sequence from the image to represent its contents. This method proposes an image hash which is generated from Haralick and MOD-LBP features along with luminance and chrominance, which are computed from Zernike moments. Sender generates the hash from image features and attaches it with the image to be sent....
Context in source publication
Context 1
... proposed hash can tolerate contrast adjustment and brightness adjustment up to 20 and 10 respectively. Table 1 shows the tampering detection and tampering localization results of 4 hashing methods viz. method based on Haralick, MOD-LBP and Zernike moments, method based on Haralick and Zernike moments only, method based on MOD-LBP and Zernike moments only, and method proposed in "robust hashing for image authentication using Zernike moments and local features" 13 , which are, for convenience, represented as follows. ...
Citations
... In 2015, Sebastian et al. [25] proposed a technique for hashing images that use Haralick and modified local binary pattern features, as well as luminance and chrominance channels. In the same year, Ouyang et al. [26] utilized logpolar and Quadrature DFT transform for the generation of image hash, but both these algorithms are sensitive to geometric operations. ...
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 f1 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.
... An important step in checking the feasibility of a new method is the assessment of results and the comparison with the state-of-the-art [5,22,39,44,45,49,60,61,63,65,66]. This distinction may either be based on all of the above-mentioned output criteria, or a subset of them. ...
In the recent digitization era, image hashing is a key technology, including image recognition, authentication and manipulation detection, among many multimedia security applications. The primary challenge in hashing schemes is to extract its robust feature. For a better understanding and design of a robust image hashing algorithm, it is very crucial to look into few important parameters like discrimination, robustness, reliability, etc. This paper reflects a detailed study of the existing literature on hashing-based image authentication techniques. This work provides a systematic overview and highlights the merits and demerits associated with various state-of-the-art techniques. In particular, the basic features and categories of image authentication techniques based on hashing are explored along with their properties. Moreover, different performance measures such as output metrices, receiver operating characteristics (ROC) parameters, execution time, etc., have been discussed in this work. The paper also compares the performances of various existing algorithms related to different content preserving operations on diverse data sets. This paper summarizes all the techniques and provides the most optimum solutions in regard to image hashing techniques based on different parameters.
... As the Zernike moments are computed from the inscribed circle, hence is responsible for loss of information in image corners. This phenomenon eventually reduces sensitivity to tamper detection [36]. Karsh et al. [18] have proposed a PIH technique that exhibits invariance to rotation, scaling and translational perturbations. ...
A new Discrete Cosine Transform (DCT) domain Perceptual Image Hashing (PIH) scheme is proposed in this paper. PIH schemes are designed to extract a set of features from an image to form a compact representation that can be used for image integrity verification. A PIH scheme takes an image as the input, extracts its invariant features and constructs a fixed length output, which is called a hash value. The hash value generated by a PIH scheme is then used for image integrity verification. The basic requirement for any PIH scheme is its robustness to non-malicious distortions and discriminative ability to detect minute level of tampering. The feature extraction phase plays a major role in guaranteeing robustness and tamper detection ability of a PIH scheme. The proposed scheme fuses together the DCT and Noise Resistant Local Binary Pattern (NRLBP) to compute image hash. In this scheme, an input image is divided into non-overlapping blocks. Then, DCT of each non-overlapping block is computed to form a DCT based transformed image block. Subsequently, NRLBP is applied to calculate NRLBP histogram. Histograms of all the blocks are concatenated together to get a hash vector for a single image. It is observed that low frequency DCT coefficients inherently have quite high robustness against non-malicious distortions, hence the NRLBP features extracted from the low frequency DCT coefficients provide high robustness. Computational results exhibit that the proposed hashing scheme outperforms some of the existing hashing schemes as well as can detect localized tamper detection as small as 3% of the original image size and at the same time resilient against non-malicious distortions.
... In Kailasanathan et al [8] uses statistical measurese to generate lengthy hash code. Moments are global descriptors [9] are set of values that represent the information contained in the image. ...
... Tri.H.Nguyen et al [21] combines SVD and DWT to generate watermark but problem of localization and tamper recover was addressed. Lima Sebastian et al [9] and Yan Zhao et al [24] use global and local features to produce hash and does not address image recover in case of even accidental loss of data.Obaid et al [14] generated a watermark usinginformation of spatial and frequency domain also partial recovery of lost content is addressed using RS codes. M.F.Hashmi at el [11], combines SVM and HMM classifiers to calssify the image as authentic or not. ...
... A specific subset of moment values best describes the information of the image. Moments are used to generate [14] hash but reconstruction of completed region cannot be defined if the inverse basis function suffers from approximate errors. Many recent authentication systems used a combination of two or more hashing techniques to generate a more effective hash that best describes the content of the image. ...
... Nguyen et al. [12] generates a digital watermarking by combining DWT and SVD but was not able to localize or reconstruct the affected part in case of tampering. Yang et al. [21] generated hash based on legendre moments and Lima Sebastian et al. [14], Yan Zhao et al. [22] generated a hash based on local texture features and global Zernike moment features. It was not able to recover the loss of information on tampering. ...
The image authentication plays a vital role in modern multimedia technology. The existing technique for preserving authentication abided the content only by preserving transformations like scaling, additive noise, gamma correction, brightness adjustments, water marking etc., but it fails to authenticate larger angles rotations. Very few systems had authenticated rotations to a smaller angle which was less than 5o. In existing system, the failure occurred in authenticating large angle rotation is due to the finite divisions of equal sized square block for calculating the local features. In this paper concept of dividing circular blocks with equal area is analyzed for better competency. The Haralick features are mostly calculated for square block. In the proposed system 14 features of Haralick has been grouped to hash code for each circular blocks in sender side. In receiver side, the same procedure is followed to generate hash code and the comparison is carried out to verify the authentication. In addition to scaling, brightness, contrast adjustment, gamma correction etc., the proposed system tolerates rotation even to greater angles up to 360o with better efficiency.
... Zernike moment represents global feature and Haralick texture extracts 14 local statistics values represents local texture feature [8]. Global zernike moments combined with local MOD-LBP feature are combined [9]. Radon transformed image has both local and global features. ...
... where LoGN and LoGD are LoG weight factor of the nearest and the diagonal neighbours respectively. Final weight for the nearest and the diagonal neighbours are given by (9) and (10). ...
p>Image hashing is an efficient way to handle digital data authentication problem. Image hashing represents quality summarization of image features in compact manner. In this paper, the modified center symmetric local binary pattern (CSLBP) image hashing algorithm is proposed. Unlike CSLBP 16 bin histogram, Modified CSLBP generates 8 bin histogram without compromise on quality to generate compact hash. It has been found that, uniform quantization on a histogram with more bin results in more precision loss. To overcome quantization loss, modified CSLBP generates the two histogram of a four bin. Uniform quantization on a 4 bin histogram results in less precision loss than a 16 bin histogram. The first generated histogram represents the nearest neighbours and second one is for the diagonal neighbours. To enhance quality in terms of discrimination power, different weight factor are used during histogram generation. For the nearest and the diagonal neighbours, two local weight factors are used. One is the Standard Deviation (SD) and other is the Laplacian of Gaussian (LoG). Standard deviation represents a spread of data which captures local variation from mean. LoG is a second order derivative edge detection operator which detects edges well in presence of noise. The proposed algorithm is resilient to the various kinds of attacks. The proposed method is tested on database having malicious and non-malicious images using benchmark like NHD and ROC which confirms theoretical analysis. The experimental results shows good performance of the proposed method for various attacks despite the short hash length.</p
... Following represents various global and local features pairs for content change location locally as well as globally. DWT-SVD and Saliency object detection using spectral residual model; Projected Gradient Non-negative Matrix Factorization (PGNMF), ring partition and saliency detection; Zernike moment and Salient point detection; Zernike moment and Haralick local features; Zernike moments, MOD-LBP and Haralick texture features; Invariant moments from Radon coefficients and statistical measures from Radon coefficients; DCT coefficients of Watson's visual model and SIFT key points; Color vector angle and Salient edge points [5][6][7][8][9][10][11][12]. ...
Hashing is popular technique of image authentication to identify malicious attacks and it also allows appearance changes in an image in controlled way. Image hashing is quality summarization of images. Quality summarization implies extraction and representation of powerful low level features in compact form. Proposed adaptive CSLBP compressed hashing method uses modified CSLBP (Center Symmetric Local Binary Pattern) as a basic method for texture extraction and color weight factor derived from L*a*b* color space. Image hash is generated from image texture. Color weight factors are used adaptively in average and difference forms to enhance discrimination capability of hash. For smooth region, averaging of colours used while for non-smooth region, color differencing is used. Adaptive CSLBP histogram is a compressed form of CSLBP and its quality is improved by adaptive color weight factor. Experimental results are demonstrated with two benchmarks, normalized hamming distance and ROC characteristics. Proposed method successfully differentiate between content change and content persevering modifications for color images.
... In similar approach, global features are retrieved from Zernike moments and local features are acquired from Haralick textures [7]. Haralick texture features and MOD-LBP with Zernike moments are combined to form a hash [8]. ...
... This drawback makes these techniques unsuitable for image authentication. Robust image hashing provides a short sequence from image and can tolerate content-preserving modifications [18]. Previous studies refer to the role of robust image hashing for image authentication. ...
Image hashing is one of the multimedia protection techniques. In this paper, a new method for robust image hashing based on quaternion polar complex exponential transform (QPCET) is proposed. The proposed method targets two goals. The first goal is the robustness against geometric and common signal processing attacks. The second one is authenticating color images without conversion which keeps their color information. In the proposed method, the input color image is normalized by the bicubic interpolation and then the interpolated image passes to Gaussian low-pass filter. QPCET moments are used to extract features. Finally, the hash value is calculated using the extracted features. On the sender side, a secret key is utilized to increase the protection of the hash value before transmitting it. The hash value is attached with the transmitted color image. On the receiver side, the authenticity of the received image is checked by decrypting the hash value. Euclidean distance is used to check the similarity between different hashes. Results of the conducted experiments prove the robustness of proposed hash against different geometric and signal processing attacks. Also, it preserves the content of the transmitted color image. Hashing different images has a very low collision probability which ensure the suitability of the proposed method for image authentication. Comparison with the existing methods ensures the superiority of the proposed method.
... The output hash values change with a one-bit change of the input data [1]. Unlike traditional hashing techniques, robust image hashing can tolerate content preserving modifications and therefore, could be used in image authentication [2]. Studies have been conducted to investigate the role of robust image hashing for image authentication. ...
... In [2], Sebastin and his co-authors applied the image hashing algorithm in two successive steps. In the first step, they extracted the features of the input images using Zernike moments (ZMs). ...
Image hashing is one of the multimedia protection techniques. In this paper,a new method for robust image hashing based on quaternion polar complex exponential transform (QPCET) is proposed. The proposed method targets two goals. The first goal is the robustness against geometric and common signal processing attacks. The second one is authenticating color images without conversion which keeps their color information. In the proposed method, the input color image is normalized by the bicubic interpolation and then the interpolated image passes to Gaussian low-pass filter. QPCET moments are used to extract features. Finally, the hash value is calculated using the extracted features. On the sender side, a secret key is utilized to increase the protection of the hash value before transmitting it. The hash value is attached with the transmitted color image. On the receiver side, the authenticity of the received image is checked by decrypting the hash value. Euclidean distance is used to check the similarity between different hashes. Results of the conducted experiments prove the robustness of proposed hash against different geometric and signal processing attacks. Also, it preserves the content of the transmitted color image. Hashing different images has a very low collision probability which ensure the suitability of the proposed method for image authentication. Comparison with the existing methods ensures the superiority
of the proposed method.
