Deep Learning based Fingerprint Presentation Attack Detection: A Comprehensive Survey

The vulnerabilities of fingerprint authentication systems have raised security concerns when adapting them to highly secure access-control applications. Therefore, Fingerprint Presentation Attack Detection (FPAD) methods are essential for ensuring reliable fingerprint authentication. Owing to the lack of generation capacity of traditional handcrafted based approaches, deep learning-based FPAD has become mainstream and has achieved remarkable performance in the past decade. Existing reviews have focused more on hand-cratfed rather than deep learning-based methods, which are outdated. To stimulate future research, we will concentrate only on recent deep-learning-based FPAD methods. In this paper, we first briefly introduce the most common Presentation Attack Instruments (PAIs) and publicly available fingerprint Presentation Attack (PA) datasets. We then describe the existing deep-learning FPAD by categorizing them into contact, contactless, and smartphone-based approaches. Finally, we conclude the paper by discussing the open challenges at the current stage and emphasizing the potential future perspective.Comment: 29 pages, submitted to ACM computing survey journa

RemNet: Remnant Convolutional Neural Network for Camera Model Identification and Image Manipulation Detection

Camera model identification (CMI) and image manipulation detection are of paramount importance in image forensics as digitally altered images are becoming increasingly commonplace. In this thesis, we propose a novel convolutional neural network (CNN) architecture for performing these two crucial tasks. Our proposed Remnant Convolutional Neural Network (RemNet) is designed with emphasis given on the preprocessing task considered to be inevitable for removing the scene content that heavily obscures the camera model fingerprints and image manipulation artifacts. Unlike the conventional approaches where fixed filters are used for preprocessing, the proposed remnant blocks, when coupled with a classification block and trained end-to-end, learn to suppress the unnecessary image contents dynamically. This helps the classification block extract more robust images forensics features from the remnant of the image. We also propose a variant of the network titled L2-constrained Remnant Convolutional Neural Network (L2-constrained RemNet), where an L2 loss is applied to the output of the preprocessor block, and categorical crossentropy loss is calculated based on the output of the classification block. The whole network is trained in an end-to-end manner by minimizing the total loss, which is a combination of the L2 loss and the categorical crossentropy loss. The whole network, consisting of a preprocessing block and a shallow classification block, when trained on 18 models from the Dresden database, shows 100% accuracy for 16 camera models with an overall accuracy of 98.15% on test images from unseen devices and scenes, outperforming the state-of-the-art deep CNNs used in CMI. Furthermore, the proposed remnant blocks, when cascaded with the existing deep CNNs, e.g., ResNet, DenseNet, boost their performances by a large margin. The proposed approach proves to be very robust in identifying the source camera models, even if the original images are post-processed. It also achieves an overall accuracy of 95.49% on the IEEE Signal Processing Cup 2018 dataset, which indicates its generalizability. Furthermore, we attain an overall accuracy of 99.68% in image manipulation detection, which implies that it can be used as a general-purpose network for image forensic tasks

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

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

Digital image forensics via meta-learning and few-shot learning

Digital images are a substantial portion of the information conveyed by social media, the Internet, and television in our daily life. In recent years, digital images have become not only one of the public information carriers, but also a crucial piece of evidence. The widespread availability of low-cost, user-friendly, and potent image editing software and mobile phone applications facilitates altering images without professional expertise. Consequently, safeguarding the originality and integrity of digital images has become a difficulty. Forgers commonly use digital image manipulation to transmit misleading information. Digital image forensics investigates the irregular patterns that might result from image alteration. It is crucial to information security. Over the past several years, machine learning techniques have been effectively used to identify image forgeries. Convolutional Neural Networks(CNN) are a frequent machine learning approach. A standard CNN model could distinguish between original and manipulated images. In this dissertation, two CNN models are introduced to recognize seam carving and Gaussian filtering. Training a conventional CNN model for a new similar image forgery detection task, one must start from scratch. Additionally, many types of tampered image data are challenging to acquire or simulate. Meta-learning is an alternative learning paradigm in which a machine learning model gets experience across numerous related tasks and uses this expertise to improve its future learning performance. Few-shot learning is a method for acquiring knowledge from few data. It can classify images with as few as one or two examples per class. Inspired by meta-learning and few-shot learning, this dissertation proposed a prototypical networks model capable of resolving a collection of related image forgery detection problems. Unlike traditional CNN models, the proposed prototypical networks model does not need to be trained from scratch for a new task. Additionally, it drastically decreases the quantity of training images