778 research outputs found
Localization of JPEG double compression through multi-domain convolutional neural networks
When an attacker wants to falsify an image, in most of cases she/he will
perform a JPEG recompression. Different techniques have been developed based on
diverse theoretical assumptions but very effective solutions have not been
developed yet. Recently, machine learning based approaches have been started to
appear in the field of image forensics to solve diverse tasks such as
acquisition source identification and forgery detection. In this last case, the
aim ahead would be to get a trained neural network able, given a to-be-checked
image, to reliably localize the forged areas. With this in mind, our paper
proposes a step forward in this direction by analyzing how a single or double
JPEG compression can be revealed and localized using convolutional neural
networks (CNNs). Different kinds of input to the CNN have been taken into
consideration, and various experiments have been carried out trying also to
evidence potential issues to be further investigated.Comment: Accepted to CVPRW 2017, Workshop on Media Forensic
FaceForensics++: Learning to Detect Manipulated Facial Images
The rapid progress in synthetic image generation and manipulation has now
come to a point where it raises significant concerns for the implications
towards society. At best, this leads to a loss of trust in digital content, but
could potentially cause further harm by spreading false information or fake
news. This paper examines the realism of state-of-the-art image manipulations,
and how difficult it is to detect them, either automatically or by humans. To
standardize the evaluation of detection methods, we propose an automated
benchmark for facial manipulation detection. In particular, the benchmark is
based on DeepFakes, Face2Face, FaceSwap and NeuralTextures as prominent
representatives for facial manipulations at random compression level and size.
The benchmark is publicly available and contains a hidden test set as well as a
database of over 1.8 million manipulated images. This dataset is over an order
of magnitude larger than comparable, publicly available, forgery datasets.
Based on this data, we performed a thorough analysis of data-driven forgery
detectors. We show that the use of additional domainspecific knowledge improves
forgery detection to unprecedented accuracy, even in the presence of strong
compression, and clearly outperforms human observers.Comment: Video: https://youtu.be/x2g48Q2I2Z
Non-Facial Video Spatiotemporal Forensic Analysis Using Deep Learning Techniques
Digital content manipulation software is working as a boon for people to edit recorded video or audio content. To prevent the unethical use of such readily available altering tools, digital multimedia forensics is becoming increasingly important. Hence, this study aims to identify whether the video and audio of the given digital content are fake or real. For temporal video forgery detection, the convolutional 3D layers are used to build a model which can identify temporal forgeries with an average accuracy of 85% on the validation dataset. Also, the identification of audio forgery, using a ResNet-34 pre-trained model and the transfer learning approach, has been achieved. The proposed model achieves an accuracy of 99% with 0.3% validation loss on the validation part of the logical access dataset, which is better than earlier models in the range of 90-95% accuracy on the validation set
Employing optical flow on convolutional recurrent structures for deepfake detection
Deepfakes, or artificially generated audiovisual renderings, can be used to defame a public figure or influence public opinion. With the recent discovery of generative adversarial networks, an attacker using a normal desktop computer fitted with an off-the-shelf graphics processing unit can make renditions realistic enough to easily fool a human observer. Detecting deepfakes is thus becoming vital for reporters, social networks, and the general public. Preliminary research introduced simple, yet surprisingly efficient digital forensic methods for visual deepfake detection. These methods combined convolutional latent representations with bidirectional recurrent structures and entropy-based cost functions. The latent representations for the video are carefully chosen to extract semantically rich information from the recordings. By feeding these into a recurrent framework, we were able to sequentially detect both spatial and temporal signatures of deepfake renditions. The entropy-based cost functions work well in isolation as well as in context with traditional cost functions.
However, re-enactment based forgery is getting harder to detect with newer generation techniques ameliorating on temporal ironing and background stability. As these generative models involve the use of a learnable flow mapping network from the driving video to the target face, we hypothesized that the inclusion of edge maps in addition to dense flow maps near the facial region provides the model with finer details to make an informed classification. Methods were demonstrated on the FaceForensics++, Celeb-DF, and DFDC-mini (custom-made) video datasets, achieving new benchmarks in all categories. We also perform extensive studies to evaluate on adversaries and demonstrate generalization to new domains, consequently gaining further insight into the effectiveness of the new architectures
Preliminary Forensics Analysis of DeepFake Images
One of the most terrifying phenomenon nowadays is the DeepFake: the
possibility to automatically replace a person's face in images and videos by
exploiting algorithms based on deep learning. This paper will present a brief
overview of technologies able to produce DeepFake images of faces. A forensics
analysis of those images with standard methods will be presented: not
surprisingly state of the art techniques are not completely able to detect the
fakeness. To solve this, a preliminary idea on how to fight DeepFake images of
faces will be presented by analysing anomalies in the frequency domain.Comment: Accepted at IEEE AEIT International Annual Conference 202
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