702 research outputs found
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
Shrinking the Semantic Gap: Spatial Pooling of Local Moment Invariants for Copy-Move Forgery Detection
Copy-move forgery is a manipulation of copying and pasting specific patches
from and to an image, with potentially illegal or unethical uses. Recent
advances in the forensic methods for copy-move forgery have shown increasing
success in detection accuracy and robustness. However, for images with high
self-similarity or strong signal corruption, the existing algorithms often
exhibit inefficient processes and unreliable results. This is mainly due to the
inherent semantic gap between low-level visual representation and high-level
semantic concept. In this paper, we present a very first study of trying to
mitigate the semantic gap problem in copy-move forgery detection, with spatial
pooling of local moment invariants for midlevel image representation. Our
detection method expands the traditional works on two aspects: 1) we introduce
the bag-of-visual-words model into this field for the first time, may meaning a
new perspective of forensic study; 2) we propose a word-to-phrase feature
description and matching pipeline, covering the spatial structure and visual
saliency information of digital images. Extensive experimental results show the
superior performance of our framework over state-of-the-art algorithms in
overcoming the related problems caused by the semantic gap.Comment: 13 pages, 11 figure
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
- …