45 research outputs found
Do GANs leave artificial fingerprints?
In the last few years, generative adversarial networks (GAN) have shown
tremendous potential for a number of applications in computer vision and
related fields. With the current pace of progress, it is a sure bet they will
soon be able to generate high-quality images and videos, virtually
indistinguishable from real ones. Unfortunately, realistic GAN-generated images
pose serious threats to security, to begin with a possible flood of fake
multimedia, and multimedia forensic countermeasures are in urgent need. In this
work, we show that each GAN leaves its specific fingerprint in the images it
generates, just like real-world cameras mark acquired images with traces of
their photo-response non-uniformity pattern. Source identification experiments
with several popular GANs show such fingerprints to represent a precious asset
for forensic analyses
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
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
Perceptual Quality-preserving Black-Box Attack against Deep Learning Image Classifiers
Deep neural networks provide unprecedented performance in all image
classification problems, taking advantage of huge amounts of data available for
training. Recent studies, however, have shown their vulnerability to
adversarial attacks, spawning an intense research effort in this field. With
the aim of building better systems, new countermeasures and stronger attacks
are proposed by the day. On the attacker's side, there is growing interest for
the realistic black-box scenario, in which the user has no access to the neural
network parameters. The problem is to design efficient attacks which mislead
the neural network without compromising image quality. In this work, we propose
to perform the black-box attack along a low-distortion path, so as to improve
both the attack efficiency and the perceptual quality of the adversarial image.
Numerical experiments on real-world systems prove the effectiveness of the
proposed approach, both in benchmark classification tasks and in key
applications in biometrics and forensics.Comment: 8 pages, journa
Are GAN generated images easy to detect? A critical analysis of the state-of-the-art
The advent of deep learning has brought a significant improvement in the
quality of generated media. However, with the increased level of photorealism,
synthetic media are becoming hardly distinguishable from real ones, raising
serious concerns about the spread of fake or manipulated information over the
Internet. In this context, it is important to develop automated tools to
reliably and timely detect synthetic media. In this work, we analyze the
state-of-the-art methods for the detection of synthetic images, highlighting
the key ingredients of the most successful approaches, and comparing their
performance over existing generative architectures. We will devote special
attention to realistic and challenging scenarios, like media uploaded on social
networks or generated by new and unseen architectures, analyzing the impact of
suitable augmentation and training strategies on the detectors' generalization
ability.Comment: 7 pages, 5 figures, conferenc
SILA: a system for scientific image analysis
A great deal of the images found in scientific publications are retouched, reused, or composed to enhance the quality of the presentation. In most instances, these edits are benign and help the reader better understand the material in a paper. However, some edits are instances of scientific misconduct and undermine the integrity of the presented research. Determining the legitimacy of edits made to scientific images is an open problem that no current technology can perform satisfactorily in a fully automated fashion. It thus remains up to human experts to inspect images as part of the peer-review process. Nonetheless, image analysis technologies promise to become helpful to experts to perform such an essential yet arduous task. Therefore, we introduce SILA, a system that makes image analysis tools available to reviewers and editors in a principled way. Further, SILA is the first human-in-the-loop end-to-end system that starts by processing article PDF files, performs image manipulation detection on the automatically extracted figures, and ends with image provenance graphs expressing the relationships between the images in question, to explain potential problems. To assess its efficacy, we introduce a dataset of scientific papers from around the globe containing annotated image manipulations and inadvertent reuse, which can serve as a benchmark for the problem at hand. Qualitative and quantitative results of the system are described using this dataset