An Efficient Passive Authentication Scheme for Copy-Move Forgery Based on DCT

A robust detection algorithm for copy-move forgery in digital images

This paper proposes a blind authentication scheme to identify duplicated regions for copy-move forgery based on perceptual hashing and package clustering algorithms. For all fixed-size image blocks in suspicious images, discrete cosine transform (DCT) is used to obtain their DCT coefficient matrixes. Their perceptual hash matrixes and perceptual hash feature vectors are orderly addressed. Moreover, a package clustering algorithm is proposed to replace traditional lexicographic order algorithms for improving the detection precision. Similar blocks can be identified by matching the perceptual hash feature vectors in each package and its adjacent package. The experimental results show that the proposed scheme can locate irregular tampered regions and multiple duplicated regions in suspicious images although they are distorted by some hybrid trace hiding operations, such as adding white Gaussian noise and Gaussian blurring, adjusting contrast ratio, luminance, and hue, and their hybrid operations.

Copy-move image forgery detection based on Gabor magnitude

This paper presents a novel image watermarking algorithm based on Discrete Cosine Transform (DCT). To imbed the watermark, we first divide the image into non-overlapping partition. Then, after applying DCT, named as one-level DCT, to the host images blocks, and DC coefficients of each block are extracted. Next, we perform DCT again, named as two-level DCT, on the matrix comprised of the selected DC coefficients of each image blocks. Finally, the watermark is embedded on the part of the coefficients after the two-level DCT. The host signal interference (HIS) is the persistent concern in the spread spectrum principle. This paper overcomes the drawback by substituting spread spectrum watermark (SSW) for the original values. However, in the traditional way, these original values are modified by adding with SSW. Moreover, fixed-number blocks for image segmentation plays an important role in resisting scaling attacks. Experimental results demonstrate that the proposed method is resilient to many kinds of image processing attacks and geometric distortions, such as filtering, noise adding, JPEG compression, scaling, aspect ratio change and some kinds of combined transforms.

Content-Based Image Retrieval (CBIR) has become one of the trending areas of research in computer vision. In traditional CBIR, the features in a spatial domain, such as color, texture, shape, and point features, are extracted. It is often considered that apart from the spatial features, the features extracted from the frequency domain of the images can give further information on the features of an image. In this paper, we are proposing two novel methods for the purpose of feature extraction from the 2-dimensional Discrete Cosine Transform (DCT) of an image: DCT_256_Zigzag and DCT_256_2 × 2. These methods take into consideration the lower frequencies in order to determine the features in the frequency domain. The advantage of using the zigzag scanning is to have the maximum low frequency values having Higher Energies comparatively. These two features are combined with two of the existing spatial domain features: Local Binary Patterns (LBP) and Interchannel voting features to generate a global feature vector for an image. For a query image, its feature vector is compared with feature vectors of every other image in the database using d1-distance, and the images with least distance are considered most similar images to the query image. To evaluate the efficiency of these two methods, five standard performance measures such as Average Precision Rate (APR), Average Recall Rate (ARR), F-Measure, Average Normalized Modified Retrieval Rank (ANMRR), and Total Minimum Retrieval Epoch (TMRE) are used. Six benchmark image datasets Corel-1000, Corel-5000, Corel-10000, VisTex, STex, and Color-Brodatz are used to corroborate the performance of these methods.KeywordsCBIRDCTFrequency domainInterchannel votingLBPTMRE

The Discrete Cosine Transform (DCT) based watermarking scheme is a common and popular technique that has been used for long time for image watermarking. This technique is based on embedding watermark information in the middle frequency band of the DCT blocks of the host image. In this work, we aimed to further improve the commonly used DCT based watermarking method by combining the DCT with the PDE (Partial Differential Equation) method, while the PDE is a method based on decomposing an image into its structures, textures, and noise components. To test the robustness of our developed combined method we evaluate the proposed method against Gaussian noise, JPEG compression, Salt&Pepper and Averagefilter. Our experimental results and comparison analysis demonstrated that our method has better performance in terms of invisibility and the robustness of the watermark compared to the traditional DCT based watermarking scheme.

A337 improved forgery detection and tampering localization method is proposed for double JPEG compression images based on first digit feature of discrete cosine transform (DCT) coefficients and kernel-based nonlinear representor (KNR). First, a to-be-checked JPEG image is divided into overlapping blocks of size $64\times 64$ , and the first digit (1∼9) features of alternating current (AC) DCT coefficients at the first nine positions of every $8\times 8$ blocks are obtained in each image block, followed by principal component analysis (PCA) transform to form compact features. And then the KNR classifier is used to judge whether the corresponding image block has been re-compressed. Finally, the test results of double compression are used to locate the tampered area of JPEG image. Experimental results show that in comparison with representative algorithms, the improved algorithm achieves better results, and is robust to operations such as rotation, resizing and feathering. Moreover, KNR classifier outperforms classical SVM classifier in recognition effect and efficiency.

Image watermarking is a sophisticated method often used to assert ownership and ensure the integrity of digital images. This research aimed to propose and evaluate an advanced watermarking technique that utilizes a combination of singular value decomposition methodology and discrete cosine transformation to embed the Dian Nuswantoro University symbol as proof of ownership into digital images. Specific goals included optimizing the embedding process to ensure high fidelity of the embedded watermark and evaluating the fuzziness of the watermark to maintain the visual quality of the watermarked image. The methods used in this research were singular value decomposition and discrete cosine transformation, which are implemented because of their complementary strengths. Singular value decomposition offers robustness and stability, while discrete cosine transformation provides efficient frequency domain transformation, thereby increasing the overall effectiveness of the watermarking process. The results of this study showed the efficacy of the Lena image technique in gray scale having a mean square error of 0.0001, a high peak signal-to-noise ratio of 89.13 decibels (dB), a universal quality index of 0.9945, and a similarity index structural of 0.999. These findings confirmed that the proposed approach maintains image quality while providing watermarking resistance. In conclusion, this research contributed a new watermarking technique designed to verify institutional ownership in digital images, specifically benefiting Dian Nuswantoro University. It showed significant potential for wider application in digital rights management.

This paper presents a new robust hybrid multiple watermarking technique using fusion of discrete wavelet transforms (DWT), discrete cosine transforms (DCT), and singular value decomposition (SVD) instead of applying DWT, DCT and SVD individually or combination of DWT-SVD / DCT-SVD. For identity authentication purposes, multiple watermarks are embedded into the same medical image / multimedia objects simultaneously, which provides extra level of security with acceptable performance in terms of robustness and imperceptibility. In the embedding process, the cover image is decomposed into first level discrete wavelet transforms where the A (approximation/lower frequency sub-band) is transformed by DCT and SVD. The watermark image is also transformed by DWT, DCT and SVD. The S vector of watermark information is embedded in the S component of the cover image. The watermarked image is generated by inverse SVD on modified S vector and original U, V vectors followed by inverse DCT and inverse DWT. The watermark is extracted using an extraction algorithm. Furthermore, the text watermark is embedding at the second level of the D (diagonal sub-band) of the cover image. The security of the text watermark considered as EPR (Electronic Patient Record) data is enhanced by using encryption method before embedding into the cover. The results are obtained by varying the gain factor, size of the text watermark, and cover medical images. The method has been extensively tested and analyzed against known attacks and is found to be giving superior performance for robustness, capacity and reduced storage and bandwidth requirements compared to reported techniques suggested by other authors.

In the fingerprint recognition application utilizing more information other than minutiae is much helpful. We present here a fingerprint matching scheme based on transform features and their comparison. The technique described here obviates the need for extracting minutiae points to match fingerprint images. The proposed scheme uses Discrete Cosine Transform (DCT), Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) to create feature vector for fingerprints. After finding out the core point, fingerprint image of size 64times64 is cropped around the core point. The transform is applied on the cropped image without any pre-processing. The transform coefficients are arranged in specific manner and are used to obtain the feature vector in terms of standard deviation. The fingerprint matching is based on the minimum Euclidean distance between two feature vectors. Here database is formed by capturing 8 images per person using 500 dpi optical scanner. Training images used to form feature vector are 2, 4 or 6 per person. In the matching phase either all or remaining images are checked in identification mode to find out the percentage recognition rate. Comparison for all the three transform is presented here and it is observed that DCT and DFT gives better result as compared DWT.

The performance and effectiveness of CBIR scheme are directly associated with construction of small dimensional as well as salient image features respectively. So, in this paper, we have carried out the image retrieval process with small dimensional salient Image components or features as compare to the original image size and the imaze retrieval accuracy has been improved due to the consideration of local information rather than global information of Image data. Initially, the Image data is exploited in block level by discrete cosine transformation (DCT) and subsequently, some significant DCT coefficients are selected from each block as salient image components. However, all the DCT coefficients are not equally important in terms of visual perception and their dimension is also not negligible even in Image retrieval process. Later, selected AC coefficients are divided into four different groups. Later, some statistical parameters are computed from each group. Each statistical value is placed at a particular matrix. So, from each group, the number of matrices constructed is equal to the number of statistical parameters evaluated and one more matrix for DC coefficients is considered. Further, for construction of small dimensional feature vectors, gray level co-occurrence matrix (GLCM) is employed on all constructed matrices to derive the feature vector for a color component. The same procedure is employed on all three components and all feature vectors are combined toaether to form the final feature vector. The proposed CBIR structure is tested on three standard Image database i.e. Corel-lK, GHIM-10K and Olivia database and the experimental results demonstrate satisfactorily image retrieval and performance outperform other state-of-art schemes in many instances with respect to their precision values.

In this paper, we propose Speaker Identification using the frequency distribution of various transforms like DFT (Discrete Fourier Transform), DCT (Discrete Cosine Transform), DST (Discrete Sine Transform), Hartley, Walsh, Haar and Kekre transforms. The speech signal spoken by a particular speaker is converted into frequency domain by applying the different transform techniques. The distribution in the transform domain is utilized to extract the feature vectors in the training and the matching phases. The results obtained by using all the seven transform techniques have been analyzed and compared. It can be seen that DFT, DCT, DST and Hartley transform give comparatively similar results (Above 96%). The results obtained by using Haar and Kekre transform are very poor. The best results are obtained by using DFT (97.19% for a feature vector of size 40).

In order to detect whether there is copy-move forgery in an audio file, a new algorithm is proposed in this paper. In this algorithm, some syllables are obtained by segmenting audio signal with voice activity detection (VAD) algorithm. Discrete cosine transform (DCT) is performed to process these syllables to generate DCT coefficients. Then these coefficients corresponding to every syllable are converted to a square matrix. After that, singular value decomposition (SVD) transform is performed for the square matrix to obtain singular eigenvector. Here, the contribution rate is calculated to reduce the dimension of the singular eigenvector. So the amount of data to be processed can be reduced greatly. Finally, the distances among syllables are measured by comparing the distance among the low-dimensional singular vectors to determine whether there is the copy-move relationship between corresponding syllables. Because these singular values have a great stability, the distance between two syllables would have little change after the common signal processing. The low-dimension singular eigenvector requires less computational complexity, so the algorithm costs less running time. The experiment results show that the proposed algorithm is robust against conventional attacks and has better efficiency.

In this paper a method is presented for copyright protection in digital images. Copyright protection is achieved by embedding an invisible signal, known as digital signature or watermark, in the digital image. The method proposed in this paper casts the signature in the frequency domain by slightly modifying the values of randomly selected DC coefficients of the Discrete Cosine Transform (DCT) of the image. The same method is applied also on the Subband or Wavelet Transform coefficients. An adaptive method is proposed also based on perceptual criteria that guarantees the invisibility of the watermark and avoids the deterioration of the image. Signature detection is done via hypothesis testing, without to use any information from the original image. The watermarks embedded by the proposed method are very resistant to JPEG and other frequently used compression. Experimental results using real image data verify the effectiveness of the method.

In this paper, a new method for off-line signature verification is proposed based on discrete cosine transform (DCT). The proposed approach has two stages, namely feature extraction and representation of signature using DCT followed by classification through support vector machine (SVM). The training signature samples are subjected to preprocessing to obtain binarized image, and DCT is employed on the binarized image. The upper-left corner block of size m X n is chosen as a representative feature vector for each trained signature sample. These small feature vector blocks are fed as an input to the SVM for training purpose. The SVM is used as a verification tool and trained with different number of training samples including genuine, skilled, and random forgeries. The proposed approach produces excellent results on the standard signature databases, namely CEDAR, GPDS-160, and MUKOS—a Kannada signature database. In order to demonstrate the superiority of the proposed approach, comparative analysis is provided with many of the standard approaches.KeywordsDiscrete cosine transformSupport vector machineSignature verification