A Scaling Robust Copy-Paste Tampering Detection for Digital Image Forensics

AbstractIt is crucial in image forensics to prove the authenticity of the digital images. Due to the availability of the using sophisticated image editing software programs, anyone can manipulate the images easily. There are various types of digital image manipulation or tampering possible; like image compositing, splicing, copy-paste, etc. In this paper, we propose a passive scaling robust algorithm for the detection of Copy-Paste tampering. Sometimes the copied region of an image is scaled before pasting to some other location in the image. In such cases, the normal Copy-Paste detection algorithm fails to detect the forgeries. We have implemented and used an improved customized Normalized Cross Correlation for detecting highly correlated areas from the image and the image blocks, thereby detecting the tampered regions from an image. The experimental results demonstrate that the proposed approach can be effectively used to detect copy-paste forgeries accurately and is scaling robust

Copy-Move Forgery Detection by Matching Triangles of Keypoints

Copy-move forgery is one of the most common types of tampering for digital images. Detection methods generally use block-matching approaches, which first divide the image into overlapping blocks and then extract and compare features to find similar ones, or point-based approaches, in which relevant keypoints are extracted and matched to each other to find similar areas. In this paper, we present a very novel hybrid approach, which compares triangles rather than blocks, or single points. Interest points are extracted from the image, and objects are modeled as a set of connected triangles built onto these points. Triangles are matched according to their shapes (inner angles), their content (color information), and the local feature vectors extracted onto the vertices of the triangles. Our methods are designed to be robust to geometric transformations. Results are compared with a state-of-the-art block matching method and a point-based method. Furthermore, our data set is available for use by academic researchers

Detection of Geometric Transformations in Copy-Move Forgery of Digital Images

Digital Forensics is a branch of forensic science which is related to cyber crime. It basically involves the detection, recovery and investigation of material found in digital devices. Digital images and videos plays most important role in digital forensics. They are the prime evidences of any crime scene. So the fidelity of the image is important. Digital images can be easily manipulated and edited with the help of image processing tools. Copy-move Forgery is the most primitive form of cyber attack on digital images. In Copy-move forgery a part of image (region) itself is copied and pasted into another part of the same image. The intension behind this type of attack is to “add” or “disappear” some objects from the image. Hence to break the fidelity of the image and fool the viewer. Copy-move attack is more prevalent in images having uniform texture or patterns, for e.g. sand, grass, water etc. In this thesis exact block matching is used as a detection technique. This technique is based on block matching, for these the whole image is divided into number of block and then the matching process is applied. Sometimes the copied region is processed before pasted i.e. some geometric transformations is applied on the pasted region. The transformations like scaling, rotation etc. It is not possible for human eyes to detect such kind of forgeries. Whenever forgery is done in this manner the common techniques like block matching, exhaustive search, auto-correlation and robust match etc. are not able to detect the forgery having geometric transformations. So that for identification of forged region we need some technique which are based on local features and also invariant to transformations. In this thesis SIFT is used for forgery detection. SIFT stands for Scale Invariant Feature Transform, this gives local feature points which are invariant to scales. The key points helps to find the duplicated region with different matching algorithm

Automatic Detection of Object-Based Video Forgery Using Various Groups of Pictures (GOP)

In recent years, there has been a lot of interest in detecting object-based video forgeries. There has been a lack of satisfactory performance with object-based forgery detectors until recently since a majority of them are still based on handcrafted features. There has been a great deal of interest in passive video forensics in recent years. Forgery of video encoded with advanced codec frameworks remains one of the biggest challenges in object-based forgery research. An object-based forgery detection approach is presented in this paper. To evaluate the proposed method, a derived test dataset of variable video lengths and frame sizes is also used in addition to the SYSU-OBJFORG dataset. This process's efficacy is verified by comparing its results with other methods. When tested on datasets with degraded-quality videos, the proposed framework performed better in real-life scenarios

Rotation Invariant on Harris Interest Points for Exposing Image Region Duplication Forgery

Nowadays, image forgery has become common because only an editing package software and a digital camera are required to counterfeit an image. Various fraud detection systems have been developed in accordance with the requirements of numerous applications and to address different types of image forgery. However, image fraud detection is a complicated process given that is necessary to identify the image processing tools used to counterfeit an image. Here, we describe recent developments in image fraud detection. Conventional techniques for detecting duplication forgeries have difficulty in detecting postprocessing falsification, such as grading and joint photographic expert group compression. This study proposes an algorithm that detects image falsification on the basis of Hessian features

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