5,953 research outputs found
Binary Hypothesis Testing Game with Training Data
We introduce a game-theoretic framework to study the hypothesis testing
problem, in the presence of an adversary aiming at preventing a correct
decision. Specifically, the paper considers a scenario in which an analyst has
to decide whether a test sequence has been drawn according to a probability
mass function (pmf) P_X or not. In turn, the goal of the adversary is to take a
sequence generated according to a different pmf and modify it in such a way to
induce a decision error. P_X is known only through one or more training
sequences. We derive the asymptotic equilibrium of the game under the
assumption that the analyst relies only on first order statistics of the test
sequence, and compute the asymptotic payoff of the game when the length of the
test sequence tends to infinity. We introduce the concept of
indistinguishability region, as the set of pmf's that can not be distinguished
reliably from P_X in the presence of attacks. Two different scenarios are
considered: in the first one the analyst and the adversary share the same
training sequence, in the second scenario, they rely on independent sequences.
The obtained results are compared to a version of the game in which the pmf P_X
is perfectly known to the analyst and the adversary
Secure Detection of Image Manipulation by means of Random Feature Selection
We address the problem of data-driven image manipulation detection in the
presence of an attacker with limited knowledge about the detector.
Specifically, we assume that the attacker knows the architecture of the
detector, the training data and the class of features V the detector can rely
on. In order to get an advantage in his race of arms with the attacker, the
analyst designs the detector by relying on a subset of features chosen at
random in V. Given its ignorance about the exact feature set, the adversary
attacks a version of the detector based on the entire feature set. In this way,
the effectiveness of the attack diminishes since there is no guarantee that
attacking a detector working in the full feature space will result in a
successful attack against the reduced-feature detector. We theoretically prove
that, thanks to random feature selection, the security of the detector
increases significantly at the expense of a negligible loss of performance in
the absence of attacks. We also provide an experimental validation of the
proposed procedure by focusing on the detection of two specific kinds of image
manipulations, namely adaptive histogram equalization and median filtering. The
experiments confirm the gain in security at the expense of a negligible loss of
performance in the absence of attacks
A tough nut to crack: performance measurement in specialist policing
As police forces worldwide increase the number of specialist units within their organisational structures, they require innovative performance measurement frameworks to properly evaluate the effectiveness of these units within the broader policing context. Specialist units are generally either technical in nature (eg forensics) or operational (eg drug or fraud squads).
This report describes the development of a performance measurement framework for Auckland Metropolitan Crime and Operational Support (AMCOS), a specialist policing unit of the New Zealand Police. AMCOS encompasses a range of technical and niche units supporting policing operations in New Zealand. The performance framework reflects the roles and functions of the unit and covers forensic performance measures, operations support performance measures, intelligence performance measures and investigations performance measures.
The authors have written at a practical level that will assist practitioners to develop similar frameworks that can meet the needs of their specialist units, but they also reflect on analytical and theoretical aspects of performance measurement systems
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
Comparison of recovery requirements with investigation requirements for intrusion management systems
Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2002Includes bibliographical references (leaves: 52-54)Text in English; Abstract: Turkish and Englishix, 54 leavesComputer systems resources and all data contained in the system may need to be protected against the increasing number of unauthorized access, manipulation and malicious intrusions. This thesis is concerned with intrusion management systems and specially with their investigation and recovery subsystems. The goals of these systems are to investigate intrusion attempts and recover from intrusions as fast as possible. In order to achieve these goals me should observe the fact that some of the intrusion attempts will be eventually successful should be accepted and necessary precautions should be taken.After an intrusion has taken place, the focus should be on the assessment:looking at what damage has occurred, how it happened, what changes can be made to prevent such attacks in the future. In this thesis, requirements of investigation and recovery process are determined and related guidelines developed. The similarities and differences between these guidelines are explained
Digital Forensics AI: Evaluating, Standardizing and Optimizing Digital Evidence Mining Techniques
The impact of AI on numerous sectors of our society and its successes over the years indicate that it can assist in resolving a variety of complex digital forensics investigative problems. Forensics analysis can make use of machine learning models’ pattern detection and recognition capabilities to uncover hidden evidence in digital artifacts that would have been missed if conducted manually. Numerous works have proposed ways for applying AI to digital forensics; nevertheless, scepticism regarding the opacity of AI has impeded the domain’s adequate formalization and standardization. We present three critical instruments necessary for the development of sound machine-driven digital forensics methodologies in this paper. We cover various methods for evaluating, standardizing, and optimizing techniques applicable to artificial intelligence models used in digital forensics. Additionally, we describe several applications of these instruments in digital forensics, emphasizing their strengths and weaknesses that may be critical to the methods’ admissibility in a judicial process
DIPPAS: A Deep Image Prior PRNU Anonymization Scheme
Source device identification is an important topic in image forensics since
it allows to trace back the origin of an image. Its forensics counter-part is
source device anonymization, that is, to mask any trace on the image that can
be useful for identifying the source device. A typical trace exploited for
source device identification is the Photo Response Non-Uniformity (PRNU), a
noise pattern left by the device on the acquired images. In this paper, we
devise a methodology for suppressing such a trace from natural images without
significant impact on image quality. Specifically, we turn PRNU anonymization
into an optimization problem in a Deep Image Prior (DIP) framework. In a
nutshell, a Convolutional Neural Network (CNN) acts as generator and returns an
image that is anonymized with respect to the source PRNU, still maintaining
high visual quality. With respect to widely-adopted deep learning paradigms,
our proposed CNN is not trained on a set of input-target pairs of images.
Instead, it is optimized to reconstruct the PRNU-free image from the original
image under analysis itself. This makes the approach particularly suitable in
scenarios where large heterogeneous databases are analyzed and prevents any
problem due to lack of generalization. Through numerical examples on publicly
available datasets, we prove our methodology to be effective compared to
state-of-the-art techniques
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
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