A novel Analysis of Image Forgery Detection Using SVM

This paper deals with basic information regarding the face recognition and whole parameters that effects the face structure and face shape. For the calculation of age, clients utilize age function combined with aging way. Face recognition is most difficult field of pattern recognition, however research in this field almost attains constancy with new difficulties emerges with time, and the research again towards the problem encounters due to aging, an automatic age technique utilized for strong face recognition is given briefly. Then user use age, commonly vector generating function or feature vector of real image to create synthesized feature vectors at target age. User uses a structure and texture vectors to show a facial image by projecting it in Eigen space of shape or texture. Images in courtrooms for evidence, graphics in newspapers and magazines, and digital graphics used by doctors are few instances that needs for pictures and not using a manipulation. Earlier, SVM algorithm failed in many instances in detection of forged picture. For the reason that single characteristic extraction algorithm, just isn#39t capable to include the certain function of the pictures. So you can overcome drawbacks of existing algorithm. We can use meta-fusion technique of HOG and Sasi elements classifier also to beat the drawback of SVM classifier.nbs

An improved discrete cosine transformation block based scheme for copy-move image forgery detection

Copy-moved forgery is a common method to manipulate images. Several attempts of image forgery have been discovered and involves a region been duplicated and copied and pasted on another region of the same image in other to achieve selfish gain. Generally, there are two classification of copy-move forgery detection technique such as the block-based and key point-based. The block-based division is mostly used and divides image into blocks during the stage of image pre-processing before features are extracted, whereas key-point based technique skips the division of image into blocks and directly extracts different local feature from the image. In this paper, we review various block based and key point approach which has been proposed by various researchers. There is a problem of achieving a balance between improving the detection accuracy and having minimal computational complexity. The proposed technique is based on an improved DCT based copy-move image forgery detection (IDB-CFD), which involves using an octagonal block to reduce the number of features for matching, thereby improving detection accuracy while having minimal complexity. The analysis of this work as compared to previous proposed works which is based on a robust detection algorithm for copy-move image forgery (RDA-CF) and involves using circle block to reduce the number of features, results show that previous work represents about 79% of the quantized DCT coefficients on each image block and this proposed work represents about 85% of quantized DCT coefficients, therefore, recovery of about 6% more features using the IDB-CFD technique was observed as the improvement over the previously proposed RDA-CF

Detecting Image Brush Editing Using the Discarded Coefficients and Intentions

This paper describes a quick and simple method to detect brush editing in JPEG images. The novelty of the proposed method is based on detecting the discarded coefficients during the quantization of the image. Another novelty of this paper is the development of a subjective metric named intentions. The method directly analyzes the allegedly tampered image and generates a forgery mask indicating forgery evidence for each image block. The experiments show that our method works especially well in detecting brush strokes, and it works reasonably well with added captions and image splicing. However, the method is less effective detecting copy-moved and blurred regions. This means that our method can effectively contribute to implementing a complete imagetampering detection tool. The editing operations for which our method is less effective can be complemented with methods more adequate to detect them

Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image Forgeries

With advanced image journaling tools, one can easily alter the semantic meaning of an image by exploiting certain manipulation techniques such as copy-clone, object splicing, and removal, which mislead the viewers. In contrast, the identification of these manipulations becomes a very challenging task as manipulated regions are not visually apparent. This paper proposes a high-confidence manipulation localization architecture which utilizes resampling features, Long-Short Term Memory (LSTM) cells, and encoder-decoder network to segment out manipulated regions from non-manipulated ones. Resampling features are used to capture artifacts like JPEG quality loss, upsampling, downsampling, rotation, and shearing. The proposed network exploits larger receptive fields (spatial maps) and frequency domain correlation to analyze the discriminative characteristics between manipulated and non-manipulated regions by incorporating encoder and LSTM network. Finally, decoder network learns the mapping from low-resolution feature maps to pixel-wise predictions for image tamper localization. With predicted mask provided by final layer (softmax) of the proposed architecture, end-to-end training is performed to learn the network parameters through back-propagation using ground-truth masks. Furthermore, a large image splicing dataset is introduced to guide the training process. The proposed method is capable of localizing image manipulations at pixel level with high precision, which is demonstrated through rigorous experimentation on three diverse datasets

Copy-Move Forgery Detection Technique for Forensic Analysis in Digital Images

Due to the powerful image editing tools images are open to several manipulations; therefore, their authenticity is becoming questionable especially when images have influential power, for example, in a court of law, news reports, and insurance claims. Image forensic techniques determine the integrity of images by applying various high-tech mechanisms developed in the literature. In this paper, the images are analyzed for a particular type of forgery where a region of an image is copied and pasted onto the same image to create a duplication or to conceal some existing objects. To detect the copy-move forgery attack, images are first divided into overlapping square blocks and DCT components are adopted as the block representations. Due to the high dimensional nature of the feature space, Gaussian RBF kernel PCA is applied to achieve the reduced dimensional feature vector representation that also improved the efficiency during the feature matching. Extensive experiments are performed to evaluate the proposed method in comparison to state of the art. The experimental results reveal that the proposed technique precisely determines the copy-move forgery even when the images are contaminated with blurring, noise, and compression and can effectively detect multiple copy-move forgeries. Hence, the proposed technique provides a computationally efficient and reliable way of copy-move forgery detection that increases the credibility of images in evidence centered applications

Comparative Analysis of Techniques Used to Detect Copy-Move Tampering for Real-World Electronic Images

Evolution of high computational powerful computers, easy availability of several innovative editing software package and high-definition quality-based image capturing tools follows to effortless result in producing image forgery. Though, threats for security and misinterpretation of digital images and scenes have been observed to be happened since a long period and also a lot of research has been established in developing diverse techniques to authenticate the digital images. On the contrary, the research in this region is not limited to checking the validity of digital photos but also to exploring the specific signs of distortion or forgery. This analysis would not require additional prior information of intrinsic content of corresponding digital image or prior embedding of watermarks. In this paper, recent growth in the area of digital image tampering identification have been discussed along with benchmarking study has been shown with qualitative and quantitative results. With variety of methodologies and concepts, different applications of forgery detection have been discussed with corresponding outcomes especially using machine and deep learning methods in order to develop efficient automated forgery detection system. The future applications and development of advanced soft-computing based techniques in digital image forgery tampering has been discussed

Comparative Analysis of Techniques Used to Detect Copy-Move Tampering for Real-World Electronic Images

Evolution of high computational powerful computers, easy availability of several innovative editing software package and high-definition quality-based image capturing tools follows to effortless result in producing image forgery. Though, threats for security and misinterpretation of digital images and scenes have been observed to be happened since a long period and also a lot of research has been established in developing diverse techniques to authenticate the digital images. On the contrary, the research in this region is not limited to checking the validity of digital photos but also to exploring the specific signs of distortion or forgery. This analysis would not require additional prior information of intrinsic content of corresponding digital image or prior embedding of watermarks. In this paper, recent growth in the area of digital image tampering identification have been discussed along with benchmarking study has been shown with qualitative and quantitative results. With variety of methodologies and concepts, different applications of forgery detection have been discussed with corresponding outcomes especially using machine and deep learning methods in order to develop efficient automated forgery detection system. The future applications and development of advanced soft-computing based techniques in digital image forgery tampering has been discussed

Accurate and Robust Localization of Duplicated Region in Copy-Move Image Forgery

Many studies in image forgery detection have demonstrated the importance of reviewing different types of the forgeries and possible detection methods. Region duplication is an efficient operation to create image manipulation. In this type of tampering a region in an image is copied and moved onto a different area of the same image. Post processing approaches, such as possible geometrical and illumination adjustments can be applied in the duplication region to create a consistent image and to hide the forgery. The local feature extraction methods have powerful recognition and point matching applications. Since, there is a duplicated area in the copy move forged images; the local feature extraction methods can present a good outcome in detecting the forgeries. There are traditional methods in this area that used feature extraction advantages to detect the copy-move forgery in images. However, the methods mostly evaluate their accuracy in a small size dataset and without comparing with other existing methods. Moreover, locating the duplication area is the second necessarily step, after detecting and distinguishing the forged images and non-forged ones. This step is not investigated enough in the literatures. In this thesis, the use of feature extraction methods in both detection and localization stages is studied. The efficiency of presented method was verified in a large dataset including different combinations of affine transform operation. The final results were compared with the existing methods, thereby revealing more accuracy in the detection of such image forgery

Detection of copy-move forgery in digital images using different computer vision approaches

Image forgery detection approaches are many and varied, but they generally all serve the same objectives: detect and localize the forgery. Copy-move forgery detection (CMFD) is widely spread and must challenge approach. In this thesis, We first investigate the problems and the challenges of the existed algorithms to detect copy-move forgery in digital images and then we propose integrating multiple forensic strategies to overcome these problems and increase the efficiency of detecting and localizing forgery based on the same image input source. Test and evaluate our copy-move forgery detector algorithm presented the outcome that has been enhanced by various computer vision field techniques. Because digital image forgery is a growing problem due to the increase in readily-available technology that makes the process relatively easy for forgers, we propose strategies and applications based on the PatchMatch algorithm and deep neural network learning (DNN). We further focus on the convolutional neural network (CNN) architecture approach in a generative adversarial network (GAN) and transfer learning environment. The F-measure score (FM), recall, precision, accuracy, and efficiency are calculated in the proposed algorithms and compared with a selection of literature algorithms using the same evaluation function in order to make a fair evaluation. The FM score achieves 0.98, with an efficiency rate exceeding 90.5% in most cases of active and passive forgery detection tasks, indicating that the proposed methods are highly robust. The output results show the high efficiency of detecting and localizing the forgery across different image formats for active and passive forgery detection. Therefore, the proposed methods in this research successfully overcome the main investigated issues in copy-move forgery detection as such: First, increase efficiency in copy-move forgery detection under a wide range of manipulation process to a copy-moved image. Second, detect and localized the copy-move forgery patches versus the pristine patches in the forged image. Finally, our experiments show the overall validation accuracy based on the proposed deep learning approach is 90%, according to the iteration limit. Further enhancement of the deep learning and learning transfer approach is recommended for future work