Digital Image Forgery Research Articles

Digital Image Forgery Detection based on Texture Feature and Clustering Technique

Digital Image Forgery Detection based on Texture Feature and Clustering Technique

Rotation and scale invariant upsampled log-polar fourier descriptor for copy-move forgery detection

Digital image forgery is becoming increasingly popular with the rapid progress of digital media editing tools. Copy-move forgery (CMF) is one of the most common methods of digital image forgery. Fo...

Fusion of block and keypoints based approaches for effective copy-move image forgery detection

Keypoint-based and block-based methods are two main categories of techniques for detecting copy-move forged images, one of the most common digital image forgery schemes. In general, block-based methods suffer from high computational cost due to the large number of image blocks used and fail to handle geometric transformations. On the contrary, keypoint-based approaches can overcome these two drawbacks yet are found difficult to deal with smooth regions. As a result, fusion of these two approaches is proposed for effective copy-move forgery detection. First, our scheme adaptively determines an appropriate initial size of regions to segment the image into non-overlapped regions. Feature points are extracted as keypoints using the scale invariant feature transform (SIFT) from the image. The ratio between the number of keypoints and the total number of pixels in that region is used to classify the region into smooth or non-smooth (keypoints) regions. Accordingly, block based approach using Zernike moments and keypoint based approach using SIFT along with filtering and post-processing are respectively applied to these two kinds of regions for effective forgery detection. Experimental results show that the proposed fusion scheme outperforms the keypoint-based method in reliability of detection and the block-based method in efficiency.

Image Forgery and Detection of Copy Move Forgery in Digital Images: A Survey of Recent Forgery Detection Techniques

With the development of Image processing editing tools and software, an image is manipulated very easily. The image manipulation detection is essential for the reason that an image can be employed as legal evidence, in the field of forensics investigations, and also in numerous various other fields. The image forgery detection based on pixels aims to validate the digital image authenticity with no aforementioned information of the main image. There are several means intended for tampering a digital image, for example, copymove or splicing, resampling a digital image (stretch, rotate, resize), removal as well as the addition of an object from your image. Copy move image forgery detection is utilized to figure out the replicated regions as well as the pasted parts, however forgery detection may possibly vary dependant on whether or not there is virtually any post-processing on the replicated part before inserting the item completely to another party. Typically, counterfeiters utilize many operations like rotation, filtering, JPEG compression, resizing as well as the addition of noise to the main image before pasting, that make this thing challenging to recognize the copy move image forgery. Hence, forgery detector needs to be robust to any or all manipulations and also the latest editing software tools. In the literature part, various researchers portrayed the working scenario of copy-move image forgery utilizing the similarity measures as well as the relationship among the original parts of the image and their pasted parts in the similar image. This research paper illustrates recent issues in the techniques of forgery detection and also all their comparative analysis.

Blind Detection of Region Duplication Forgery Using Fractal Coding and Feature Matching.

Digital image forgery detection is important because of its wide use in applications such as medical diagnosis, legal investigations, and entertainment. Copy-move forgery is one of the famous techniques, which is used in region duplication. Many of the existing copy-move detection algorithms cannot effectively blind detect duplicated regions that are made by powerful image manipulation software like Photoshop. In this study, a new method is proposed for blind detecting manipulations in digital images based on modified fractal coding and feature vector matching. The proposed method not only detects typical copy-move forgery, but also finds multiple copied forgery regions for images that are subjected to rotation, scaling, reflection, and a mixture of these postprocessing operations. The proposed method is robust against tampered images undergoing attacks such as Gaussian blurring, contrast scaling, and brightness adjustment. The experimental results demonstrated the validity and efficiency of the method.

A Fast and Robust Approach to Detect Copy-Move Forgery in Digital Images

the present world, digital images and videos are our main source of information and these can be easily manipulated to conceal some meaningful information by using largely available powerful and sophisticated image editing tools. So in this era of illusions, verifying the authenticity of images and locating the tampering regions without using any prior knowledge is an important area of research. Copy-move forgery is one of the mostly used forgery technique. Many block matching algorithms are suggested to deal with this type of forgery but still there are some issues which are not properly addressed and need more attention such as time complexity. With increasing image size the execution time of detection algorithm is also increases. In this paper, we propose a method based on Discrete Cosine Transform (DCT) in order to improve time complexity. The proposed technique can also detect forgery even after some post processing operations such as rotation and Gaussian noise addition. Keywords-move forgery; Discrete Cosine Transform(DCT); Block matching; passive forgery detection; image forgery

Multi-Level Dense Descriptor and Hierarchical Feature Matching for Copy–Move Forgery Detection

In this paper, a Multi-Level Dense Descriptor (MLDD) extraction method and a Hierarchical Feature Matching method are proposed to detect copy–move forgery in digital images. The MLDD extraction method extracts the dense feature descriptors using multiple levels, while the extracted dense descriptor consists of two parts: the Color Texture Descriptor and the Invariant Moment Descriptor. After calculating the MLDD for each pixel, the Hierarchical Feature Matching method subsequently detects forgery regions in the input image. First, the pixels that have similar color textures are grouped together into distinctive neighbor pixel sets. Next, each pixel is matched with pixels in its corresponding neighbor pixel set through its geometric invariant moments. Then, the redundant pixels from previously generated matched pixel pairs are filtered out by the proposed Adaptive Distance and Orientation Based Filtering method. Finally, some morphological operations are applied to generate the final detected forgery regions. Experimental results show that the proposed scheme can achieve much better detection results compared with the existing state-of-the-art CMFD methods, even under various challenging conditions such as geometric transforms, JPEG compression, noise addition and down-sampling.

Non-Overlapping Block-based Parametric Forgery Detection Model

Modification of a digital image by adding or removing some of its elements using a wide variety of image processing tools results in image forgery. As a result authentication of originality of a digital image is becoming a challenging task. Copy-paste forgery is one of the forgeries belonging to context based forgery. Copy-Paste Forgery Detection (CPFD) aims at finding regions that have been copied and pasted within the same or different image. A small change in the image may change statistical parameters that can be analysed for initial assessment of the forgery. In the present research study, a parametric forgery detection model using nonoverlapping block-based technique is developed to ascertain the copy-paste forgery in a given digital image. Statistical parameters of the input image are computed, analysed and compared with those of the forged image. The results show that the proposed model identifies the forged area of the given image and works well with low to moderate copy-paste forgery. The results obtained can be used as the initial verification of the images for forgery and to enhance the forgery detection process by identifying most likely cases of possible image forgeries. The proposed model is tested with large domain of images having different dimensions and for detecting forgery within an image. However, the model has limitations with certain geometrical transformations. General Terms Digital Image Forgery Detection Techniques.

Detection of Seam-Carving Image Based on Benford's Law for Forensic Applications

Seam-Carving is widely used for content-aware image resizing. To cope with the digital image forgery caused by Seam-Carving, a new detecting algorithm based on Benford's law is presented. The algorithm utilize the probabilities of the first digits of quantized DCT coefficients from individual AC modes to detect Seam-Carving images. The experimental result shows that the performance of proposed method is better than that of the method based on traditional Markov features and other existing methods.

ACCURACY Detection of Digital Image Forgery by Using Ant Colony Optimization Technique

Image forgery is one of the well known fields in which researches continuously exploring new areas. In digital image forgery one can change image in many ways using several software’s, researchers exploring new algorithms to detect image forgery areas and change it to original pixel values if possible. In this paper we employed ACO (Ant Colony Optimization) to find areas which are manipulated with some software. The experimental results prove that ACO is better than existing methods of detecting tampered regions in digital photo images.

Passive Copy-Move Forgery Detection using SIFT, HOG and SURF Features

Copy-move is a common type of digital image forgery. In an image, Copy-Move tampering might be done to hide an undesirable region, or to duplicate something in the image. These images might be used for necessary purpose like evidence in the court of law. So, authenticity verification plays a vital role for digital images. In this paper, we compare the CMFD (Copy-Move Forgery Detection) using Image features like SIFT (Scale Invariant Features Transform), HOG (Histogram Oriented Gradient) and SURF (Speed-Up Robust Features) and hybrid features (SURF-HOG and SIFT-HOG). The comparison results show that CMFD using SIFT features provide better results as compared with SURF and HOG features. Also, considering hybrid features, SIFT-HOG and SURF-HOG produce better results for CMFD using SIFT, SURF or HOG alone. Keywords: Passive Forensics, Copy-Move Forgery Detection, Single Region CMFD, Multiple Region CMFD, Histogram Oriented Gradient, Scale Invariant Features Transform, Speed-Up Robust Features.

Image copy–move forgery detection based on sped-up robust features descriptor and adaptive minimal–maximal suppression

Region duplication is a simple and effective operation to create digital image forgeries, where a continuous portion of pixels in an image is copied and pasted to a different location in the same image. Many prior copy–move forgery detection methods suffer from their inability to detect the duplicated region, which is subjected to various geometric transformations. A keypoint-based approach is proposed to detect the copy–move forgery in an image. Our method starts by extracting the keypoints through a fast Hessian detector. Then the adaptive minimal–maximal suppression (AMMS) strategy is developed for distributing the keypoints evenly throughout an image. By using AMMS and a sped-up robust feature descriptor, the proposed method is able to deal with the problem of insufficient keypoints in the almost uniform area. Finally, the geometric transformation performed in cloning is recovered by using the maximum likelihood estimation of the homography. Experimental results show the efficacy of this technique in detecting copy–move forgeries and estimating the geometric transformation parameters. Compared with the state of the art, our approach obtains a higher true positive rate and a lower false positive rate.

A Survey Of Digital Image Tampering Techniques

Due to powerful computers and advanced photo-editing software tools the manipulation of images has become an easy task. Confirming the authenticity of images and detecting tampered regions in an image without any knowledge about the image content is an important part of the research field. An effort is made to survey the recent advancements being made in the field of digital image forgery detection and thus passive methods for forgery detection are being presented. Blind or passive methods do not require any explicit former information about the image. In the first part, various image forgery detection techniques are classified and then an overview of passive image authentication is presented and the existing blind forgery detection techniques are reviewed.

Detection of Duplicate Region and Hybrid Non-Local Means Filtering for Denoising with Quantization Matrix Estimation for JPEG Error Analysis

Background: The existing JPEG error analysis schemes do not offer agreeable results particularly when the duplicated area is small. Region duplication is an uncomplicated and efficient process to produce digital image forgeries, where a constant segment of pixels in an image, following feasible geometrical and illumination transformations are copied and pasted to a different location in the same image. Methods: In this research work, JPEG error analysis scheme is introduced for the purpose of consistent recognition of duplicated and distorted areas in a JPEG digital image forensics. Here, presented a new Multi-directional Curvelet Transform with Fourier Transform matching Invariant Rotation (MCFTIR) region duplication detection scheme to identify duplicated regions for JPEG images. This scheme begins with estimating the overlapping blocks of a JPEG image and it is organized in accordance with the statistics of multiple curvelet sub-bands. During the second phase, the amount of candidate block pairs of JPEG images has been significantly diminished by means of spatial distance between each pair of blocks for JPEG image. For duplicate region removed images theoretically analyzing the effects of these errors on single and double JPEG compression, with five major phases like Shape-Preserving Image Resizing (SPIR) scheme for the purpose of image resizing, noises are appended to image and eliminated with the help of Hybrid Non-Local Means Filtering (HNLMF) denoising framework, Image compression through Discrete Cosine Transform – Singular Value Decomposition (DCT-SVD) was computed for single and double image compression, images were quantized by means of numerous quantization matrices, quantization matrix results are estimated with Mamdani model based Adaptive Neural Fuzzy Inference System (MANFIS) and detecting the quantization table of a JPEG image. Findings: The proposed MCFTIR methods significantly outperform existing techniques in terms of the parameters like Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) especially for the images of small sizes. It also show that the new MCFTIR method can consistently identify JPEG image blocks which are as tiny as 8x8 pixels and compressed through quality factors as elevated as 98. This performance is significant for the purpose of analyzing and locating small tampered regions inside a composite image.

Detection of copy–move forgery using discrete analytical Fourier–Mellin transform

In recent years, digital image processing has become commonplace with growing powerful and available image editing software. People without any professional technique can also manipulate and forge digital images easily. One of the most popular manners of digital image forgeries is the copy–move image forgery. Extensive researches in detecting copy–move forgery have made a deal of achievements, but most presented methods based on these researches have been only focus on some simple composite forgeries and not able to detect different types of post-processed forgeries. In this paper, we aim to deal with the post-processed forgery operations and scenarios, mainly geometric distortion. We introduce analytical Fourier–Mellin transform (AFMT) and focus on its discretization. We propose discrete analytical Fourier–Mellin transform (DAFMT). We also pay attention to high performance of DAFMT in detecting the copy–move image forgeries. Due to the AFMT described in polar coordinate, so we need to convert coordinate system from polar to Cartesian coordinates. To be computed conveniently, we define an auxiliary disk template to accomplish this conversion. We devote to the use of our proposed DAFMT in detection of image forgeries. A great deal of researches and experiments show that the proposed DAFMT can effectively resist translation, rotation, scaling, and added Gaussian noise operations. Compared with other relevant up-to-date methods, experiments also prove that DAFMT has made a progress in detecting and identifying the forgery images which are suffered from geometric distortion operations.

Copy-move image forgery detection based on Gabor magnitude

With advancement of media editing software, even people who are not image processing experts can easily alter digital images. Various methods of digital image forgery exist, such as image splicing, copy-move forgery, and image retouching. The most common method of tampering with a digital image is copy-move forgery, in which a part of an image is duplicated and used to substitute another part of the same image at a different location. In this paper, we present an efficient and robust method to detect such artifacts. First, the tampered image is segmented into overlapping fixed-size blocks, and the Gabor filter is applied to each block. Thus, the image of Gabor magnitude represents each block. Secondly, statistical features are extracted from the histogram of orientated Gabor magnitude (HOGM) of overlapping blocks, and reduced features are generated for similarity measurement. Finally, feature vectors are sorted lexicographically, and duplicated image blocks are identified by finding similarity block pairs after suitable post-processing. To enhance the algorithm’s robustness, a few parameters are proposed for removing the wrong similar blocks. Experiment results demonstrate the ability of the proposed method to detect multiple examples of copy-move forgery and precisely locate the duplicated regions, even when dealing with images distorted by slight rotation and scaling, JPEG compression, blurring, and brightness adjustment.

Passive Copy- Move Forgery Detection Using Speed-Up Robust Features, Histogram Oriented Gradients and Scale Invariant Feature Transform

Copy-Move is one of the most common technique for digital image tampering or forgery. Copy-Move in an image might be done to duplicate something or to hide an undesirable region. In some cases where these images are used for important purposes such as evidence in court of law, it is important to verify their authenticity. In this paper the authors propose a novel method to detect single region Copy-Move Forgery Detection (CMFD) using Speed-Up Robust Features (SURF), Histogram Oriented Gradient (HOG), Scale Invariant Features Transform (SIFT), and hybrid features such as SURF-HOG and SIFT-HOG. SIFT and SURF image features are immune to various transformations like rotation, scaling, translation, so SIFT and SURF image features help in detecting Copy-Move regions more accurately in compared to other image features. Further the authors have detected multiple regions COPY-MOVE forgery using SURF and SIFT image features. Experimental results demonstrate commendable performance of proposed methods.

Image Forgery Detection using Multidimensional Spectral Hashing based Polar Cosine Transform

The work aims at developing an algorithm for passive detection of copy move forgery in digital images. Accessibility and manipulability of the diverse and sophisticated digital image editing and processing tools has made the integrity of digital images debatable. The sensitivity and importance of the very many applications that revolve around digital images demand absolute authenticity of the digital images. As much simple as image tampering been made by the image editing tools, that much difficult the tampering detection process has become. In this work, a tamper detection method has been proposed that employs polar cosine transform for feature extraction and multi-dimensional spectral hashing for feature matching. The algorithm starts by dividing the image into overlapping patches and then feature vectors are extracted from the patches using Polar Cosine Transform (PCT) and similar patches are identified using multi-dimensional spectral hashing. Finally, post-verification is done to filter out false detection of forged regions. The multidimensional spectral hashing based method uses the outer product Eigen function to improve the performance of similar patch identification. The performance of the algorithm is measured in terms of precision F1 score and recall. Experimental results show that the multi-dimensional spectral hashing based identification of similar patches gives better results compared to some of the existing hashing based techniques. The proposed method proves to be effective in authenticating digital images which are employed in many fields like forensics, medicine, mass media etc.

IJARCCE - Computer and Communication Engineering

5 Abstract: With the growth of image processing technology, it is difficult to distinguish between photographic images (PIM) and photorealistic computer generated images PRCG. Watermarking was the previous technic for authentication purpose. The Demosaicing algorithm is used to detect the validity of digital images and image forgery detection.The main aim of this paper is to provide system which will help to detect the manipulation in the images.Due to technological changes, the computer generated images become more realistic. In order to distinguish between computer generated images and digital camera images we implement this approach.The whole system is controlled by administrator module.Most digital cameras employ an image sensor with a color filter array. The process of Demosaicing interposes the raw image for allocating color channels for each pixels. With proper analysis, traces of Demosaicing are revealed in the peak of an analysis signal. Theexistence of Demosaicing indicates the image is from a digital camera rather than generated by a computer. Keyword: Demosaicing, Forgery, CFA (Color Filter Array), PIM (Photo Graphic image), PRCG (Photo Realistic Computer Generated image).

IMAGE SPLICING DETECTION BASED ON DEMOSAICKING AND WAVELET TRANSFORMATION

Image splicing is a form of digital image manipulation by combining two or more image into a new image. The application was developed through a passive approach using demosaicking and wavelet transformation method. This research purposed a method to implement the demosaicking and wavelet transform for digital image forgery detection with a passive approach. This research shows that (1) demosaicking can be used as a comparison image in forgery detection; (2) the application of demosaicking and wavelet transformation can improve the quality of the input image (3) demosaicking and wavelet algorithm are able to estimate whether the input image is real or fake image with a passive approach and estimate the manipulation area from the input image.