An Evaluation of Popular Copy-Move Forgery Detection Approaches

A copy-move forgery is created by copying and pasting content within the same image, and potentially post-processing it. In recent years, the detection of copy-move forgeries has become one of the most actively researched topics in blind image forensics. A considerable number of different algorithms have been proposed focusing on different types of postprocessed copies. In this paper, we aim to answer which copy-move forgery detection algorithms and processing steps (e.g., matching, filtering, outlier detection, affine transformation estimation) perform best in various postprocessing scenarios. The focus of our analysis is to evaluate the performance of previously proposed feature sets. We achieve this by casting existing algorithms in a common pipeline. In this paper, we examined the 15 most prominent feature sets. We analyzed the detection performance on a per-image basis and on a per-pixel basis. We created a challenging real-world copy-move dataset, and a software framework for systematic image manipulation. Experiments show, that the keypoint-based features SIFT and SURF, as well as the block-based DCT, DWT, KPCA, PCA and Zernike features perform very well. These feature sets exhibit the best robustness against various noise sources and downsampling, while reliably identifying the copied regions.Comment: Main paper: 14 pages, supplemental material: 12 pages, main paper appeared in IEEE Transaction on Information Forensics and Securit

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

Copy move Forgery Detection Approaches: A Survey

Copy-move forgery detection is one of the most popular image forgery technique in which a part of a digital image is copied and pasted to another part in the same image with the intension to make an object “disappear†from the image by covering it with a small block copied from another part of the same image. Hence, the main task of copy-move forgery detection is to detect image areas that are same or almost similar within an image. These method in general use two approaches namely key-point based and block based. This paper provides a review of copy move forgery detection on various techniques.Keywords: Copy move forgery, Lexicographical Sorting, Digital Image Forgery, Duplicated Region

Copy-move Forgery Detection via Texture Description

Copy-move forgery is one of the most common type of tampering in digital images. Copy-moves are parts of the image that are copied and pasted onto another part of the same image. Detection methods in general use block-matching methods, which first divide the image into overlapping blocks and then extract features from each block, assuming similar blocks will yield similar features. In this paper we present a block-based approach which exploits texture as feature to be extracted from blocks. Our goal is to study if texture is well suited for the specific application, and to compare performance of several texture descriptors. Tests have been made on both uncompressed and JPEG compressed images

Deteksi Pemalsuan Copy-move Duplicated Region Pada Citra Digital Dengan Komputasi Numerik

Identifikasi keaslian dan integritas citra digital menjadi penting dalam forensik digital. Makalah ini mengusulkan metode pasif yang efektif untuk mendeteksi pemalsuan copy-move pada duplicated region. Implementasi metode ini dilakukan pertama-tama dengan citra input diproses dengan transformasi wavelet, lalu mengekstraksi fitur SVD pada blok citra yang telah mengalami Perubahan geometri, dan beberapa gangguan. Selanjutnya melakukan pemeriksaan kesamaan karakteristik fitur antara bagian yang disalin dan ditempelkan, setiap fitur SVD menjadi query dalam pencocokan blok citra dengan tetangga terdekat. Ekperimen menunjukkan metode ini efisien dalam komputasi, robust, dan sensitif terhadap region citra berbeda yang telah mengalami beberapa Perubahan pemprosesan citra

Detecting Copy-Move Forgery in Digital Images:A Survey and Analysis of Current Methods

As one of the most successful applications of image analysis and understanding, digital image forgery detection has recently received significant attention, especially during the past few years. At least two trend account for this: the first accepting digital image as official document has become a common practice, and the second the availability of low cost technology in which the image could be easily manipulated. Even though there are many systems to detect the digital image forgery, their success is limited by the conditions imposed by many applications. For example, detecting duplicated region that have been rotated in different angles remains largely unsolved problem. In an attempt to assist these efforts, this paper surveys the recent development in the field of Copy-Move digital image forgery detection

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