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

Audiovisual processing for sports-video summarisation technology

In this thesis a novel audiovisual feature-based scheme is proposed for the automatic summarization of sports-video content The scope of operability of the scheme is designed to encompass the wide variety o f sports genres that come under the description ‘field-sports’. Given the assumption that, in terms of conveying the narrative of a field-sports-video, score-update events constitute the most significant moments, it is proposed that their detection should thus yield a favourable summarisation solution. To this end, a generic methodology is proposed for the automatic identification of score-update events in field-sports-video content. The scheme is based on the development of robust extractors for a set of critical features, which are shown to reliably indicate their locations. The evidence gathered by the feature extractors is combined and analysed using a Support Vector Machine (SVM), which performs the event detection process. An SVM is chosen on the basis that its underlying technology represents an implementation of the latest generation of machine learning algorithms, based on the recent advances in statistical learning. Effectively, an SVM offers a solution to optimising the classification performance of a decision hypothesis, inferred from a given set of training data. Via a learning phase that utilizes a 90-hour field-sports-video trainmg-corpus, the SVM infers a score-update event model by observing patterns in the extracted feature evidence. Using a similar but distinct 90-hour evaluation corpus, the effectiveness of this model is then tested genencally across multiple genres of fieldsports- video including soccer, rugby, field hockey, hurling, and Gaelic football. The results suggest that in terms o f the summarization task, both high event retrieval and content rejection statistics are achievable

Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE

A Framework for pervasive web content delivery

Ph.DDOCTOR OF PHILOSOPH

Image and Video Forensics

Nowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security. Multimedia contents are generated in many different ways through the use of consumer electronics and high-quality digital imaging devices, such as smartphones, digital cameras, tablets, and wearable and IoT devices. The ever-increasing convenience of image acquisition has facilitated instant distribution and sharing of digital images on digital social platforms, determining a great amount of exchange data. Moreover, the pervasiveness of powerful image editing tools has allowed the manipulation of digital images for malicious or criminal ends, up to the creation of synthesized images and videos with the use of deep learning techniques. In response to these threats, the multimedia forensics community has produced major research efforts regarding the identification of the source and the detection of manipulation. In all cases (e.g., forensic investigations, fake news debunking, information warfare, and cyberattacks) where images and videos serve as critical evidence, forensic technologies that help to determine the origin, authenticity, and integrity of multimedia content can become essential tools. This book aims to collect a diverse and complementary set of articles that demonstrate new developments and applications in image and video forensics to tackle new and serious challenges to ensure media authenticity

Fast and robust image feature matching methods for computer vision applications

Service robotic systems are designed to solve tasks such as recognizing and manipulating objects, understanding natural scenes, navigating in dynamic and populated environments. It's immediately evident that such tasks cannot be modeled in all necessary details as easy as it is with industrial robot tasks; therefore, service robotic system has to have the ability to sense and interact with the surrounding physical environment through a multitude of sensors and actuators. Environment sensing is one of the core problems that limit the deployment of mobile service robots since existing sensing systems are either too slow or too expensive. Visual sensing is the most promising way to provide a cost effective solution to the mobile robot sensing problem. It's usually achieved using one or several digital cameras placed on the robot or distributed in its environment. Digital cameras are information rich sensors and are relatively inexpensive and can be used to solve a number of key problems for robotics and other autonomous intelligent systems, such as visual servoing, robot navigation, object recognition, pose estimation, and much more. The key challenges to taking advantage of this powerful and inexpensive sensor is to come up with algorithms that can reliably and quickly extract and match the useful visual information necessary to automatically interpret the environment in real-time. Although considerable research has been conducted in recent years on the development of algorithms for computer and robot vision problems, there are still open research challenges in the context of the reliability, accuracy and processing time. Scale Invariant Feature Transform (SIFT) is one of the most widely used methods that has recently attracted much attention in the computer vision community due to the fact that SIFT features are highly distinctive, and invariant to scale, rotation and illumination changes. In addition, SIFT features are relatively easy to extract and to match against a large database of local features. Generally, there are two main drawbacks of SIFT algorithm, the first drawback is that the computational complexity of the algorithm increases rapidly with the number of key-points, especially at the matching step due to the high dimensionality of the SIFT feature descriptor. The other one is that the SIFT features are not robust to large viewpoint changes. These drawbacks limit the reasonable use of SIFT algorithm for robot vision applications since they require often real-time performance and dealing with large viewpoint changes. This dissertation proposes three new approaches to address the constraints faced when using SIFT features for robot vision applications, Speeded up SIFT feature matching, robust SIFT feature matching and the inclusion of the closed loop control structure into object recognition and pose estimation systems. The proposed methods are implemented and tested on the FRIEND II/III service robotic system. The achieved results are valuable to adapt SIFT algorithm to the robot vision applications