Numerical Simulation and Design of Copy Move Image Forgery Detection Using ORB and K Means Algorithm

Copy-move is a common technique for tampering with images in the digital realm. Therefore, image security authentication is of critical importance in our society. So copy move forgery detection (CMFD) is activated in order to identify the forged portion of a photograph. A combination of the Scaled ORB and the k-means++ algorithm is used to identify this object. The first step is to identify the space on a pyramid scale, which is critical for the next step. A region's defining feature is critical to its detection. Because of this, the ORB descriptor plays an important role. Extracting FAST key points and ORB features from each scale space. The coordinates of the FAST key points have been reversed in relation to the original image. The ORB descriptors are now subjected to the k-means++ algorithm. Hammering distance is used to match the clustered features every two key points. Then, the forged key points are discovered. This information is used to draw two circles on the forged and original regions. Moment must be calculated if the forged region is rotational invariant. Geometric transformation (scaling and rotation) is possible in this method. For images that have been rotated and smoothed, this work demonstrates a method for detecting the forged region. The running time of the proposed method is less than that of the previous method

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

A review on copy-move image forgery detection techniques

With billions of digital images flooding the internet which are widely used and regards as the major information source in many fields in recent years. With the high advance of technology, it may seem easy to fraud the image. In digital images, copy-move forgery is the most common image tampering, where some object(s) or region(s) duplicate in the digital image. The important research has attracted more attention in digital forensic is forgery detection and localization. Many techniques have been proposed and many papers have been published to detect image forgery. This paper introduced a review of research papers on copy-move image forgery published in reputed journals from 2017 to 2020 and focused on discussing various strategies related with fraud images to highlight on the latest tools used in the detection. This article will help the researchers to understand the current algorithms and techniques in this field and ultimately develop new and more efficient algorithms of detection copy-move image

A Novel Approach to Detect Copy Move Forgery using Deep Learning

With the development of readily available image editing tools, manipulating an image has become a universal issue. To check the authenticity, it is necessary to identify how various images might be forged and the way they might be detected using various forgery detection approaches. The importance of detecting copy-move forgery is that it identifies the integrity of an image, which helps in fraud detection at various places such as courtrooms, news reports. This article presents an appropriate technique to detect Copy-Move forgery in which to some extent an image is copied and pasted onto an equivalent image to hide some object or to make duplication. The input image is segmented using the real-time superpixel segmentation algorithm DBSCAN (Density based spatial clustering of application with noise). Due to the high accuracy rate of the VGGNet 16 architecture, it is utilized for feature extraction of segmented images, which will also enhance the efficiency of the overall technique while matching the extracted patches using adaptive patch matching algorithm. The experimental results reveal that the proposed deep learning-based architecture is more accurate in identifying the tempered area even when the images are noisy and can save computational time as compared to existing architectures. For future research, the technique can be enhanced to work on other forgery detection techniques such as image splicing and multi-cloned images

A prediction-based approach for features aggregation in Visual Sensor Networks

Visual Sensor Networks (VSNs) constitute a key technology for the implementation of several visual analysis tasks. Recent studies have demonstrated that such tasks can be efficiently performed following an operative paradigm where cameras transmit to a central controller local image features, rather than pixel-domain images. Furthermore, features from multiple camera views may be efficiently aggregated exploiting the spatial redundancy between overlapping views. In this paper we propose a routing protocol designed for supporting aggregation of image features in a VSN. First, we identify a predictor able to estimate the efficiency of local features aggregation between different cameras in a VSN. The proposed predictor is chosen so as to minimize the prediction error while keeping the network overhead cost low. Then, we harmonically integrate the proposed predictor in the Routing Protocol for Low-Power and Lossy Networks (RPL) in order to support the task of in-network feature aggregation. We propose a RPL objective function that takes into account the predicted aggregation efficiency and build the routes from the camera nodes to a central controller so that either energy consumption or used network bandwidth is minimized. Extensive experimental results confirm that the proposed approach can be used to increase the efficiency of VSNs

Biometric Systems

Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study

Drawing, Handwriting Processing Analysis: New Advances and Challenges

International audienceDrawing and handwriting are communicational skills that are fundamental in geopolitical, ideological and technological evolutions of all time. drawingand handwriting are still useful in defining innovative applications in numerous fields. In this regard, researchers have to solve new problems like those related to the manner in which drawing and handwriting become an efficient way to command various connected objects; or to validate graphomotor skills as evident and objective sources of data useful in the study of human beings, their capabilities and their limits from birth to decline