1,638 research outputs found
Analysis of adversarial attacks against CNN-based image forgery detectors
With the ubiquitous diffusion of social networks, images are becoming a
dominant and powerful communication channel. Not surprisingly, they are also
increasingly subject to manipulations aimed at distorting information and
spreading fake news. In recent years, the scientific community has devoted
major efforts to contrast this menace, and many image forgery detectors have
been proposed. Currently, due to the success of deep learning in many
multimedia processing tasks, there is high interest towards CNN-based
detectors, and early results are already very promising. Recent studies in
computer vision, however, have shown CNNs to be highly vulnerable to
adversarial attacks, small perturbations of the input data which drive the
network towards erroneous classification. In this paper we analyze the
vulnerability of CNN-based image forensics methods to adversarial attacks,
considering several detectors and several types of attack, and testing
performance on a wide range of common manipulations, both easily and hardly
detectable
An In-Depth Study on Open-Set Camera Model Identification
Camera model identification refers to the problem of linking a picture to the
camera model used to shoot it. As this might be an enabling factor in different
forensic applications to single out possible suspects (e.g., detecting the
author of child abuse or terrorist propaganda material), many accurate camera
model attribution methods have been developed in the literature. One of their
main drawbacks, however, is the typical closed-set assumption of the problem.
This means that an investigated photograph is always assigned to one camera
model within a set of known ones present during investigation, i.e., training
time, and the fact that the picture can come from a completely unrelated camera
model during actual testing is usually ignored. Under realistic conditions, it
is not possible to assume that every picture under analysis belongs to one of
the available camera models. To deal with this issue, in this paper, we present
the first in-depth study on the possibility of solving the camera model
identification problem in open-set scenarios. Given a photograph, we aim at
detecting whether it comes from one of the known camera models of interest or
from an unknown one. We compare different feature extraction algorithms and
classifiers specially targeting open-set recognition. We also evaluate possible
open-set training protocols that can be applied along with any open-set
classifier, observing that a simple of those alternatives obtains best results.
Thorough testing on independent datasets shows that it is possible to leverage
a recently proposed convolutional neural network as feature extractor paired
with a properly trained open-set classifier aiming at solving the open-set
camera model attribution problem even to small-scale image patches, improving
over state-of-the-art available solutions.Comment: Published through IEEE Access journa
Recent Advances in Digital Image and Video Forensics, Anti-forensics and Counter Anti-forensics
Image and video forensics have recently gained increasing attention due to
the proliferation of manipulated images and videos, especially on social media
platforms, such as Twitter and Instagram, which spread disinformation and fake
news. This survey explores image and video identification and forgery detection
covering both manipulated digital media and generative media. However, media
forgery detection techniques are susceptible to anti-forensics; on the other
hand, such anti-forensics techniques can themselves be detected. We therefore
further cover both anti-forensics and counter anti-forensics techniques in
image and video. Finally, we conclude this survey by highlighting some open
problems in this domain
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
A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection
Due to limited computational and memory resources, current deep learning
models accept only rather small images in input, calling for preliminary image
resizing. This is not a problem for high-level vision problems, where
discriminative features are barely affected by resizing. On the contrary, in
image forensics, resizing tends to destroy precious high-frequency details,
impacting heavily on performance. One can avoid resizing by means of patch-wise
processing, at the cost of renouncing whole-image analysis. In this work, we
propose a CNN-based image forgery detection framework which makes decisions
based on full-resolution information gathered from the whole image. Thanks to
gradient checkpointing, the framework is trainable end-to-end with limited
memory resources and weak (image-level) supervision, allowing for the joint
optimization of all parameters. Experiments on widespread image forensics
datasets prove the good performance of the proposed approach, which largely
outperforms all baselines and all reference methods.Comment: 13 pages, 12 figures, journa
- …