88 research outputs found
Deepfake detection: humans vs. machines
Deepfake videos, where a person's face is automatically swapped with a face
of someone else, are becoming easier to generate with more realistic results.
In response to the threat such manipulations can pose to our trust in video
evidence, several large datasets of deepfake videos and many methods to detect
them were proposed recently. However, it is still unclear how realistic
deepfake videos are for an average person and whether the algorithms are
significantly better than humans at detecting them. In this paper, we present a
subjective study conducted in a crowdsourcing-like scenario, which
systematically evaluates how hard it is for humans to see if the video is
deepfake or not. For the evaluation, we used 120 different videos (60 deepfakes
and 60 originals) manually pre-selected from the Facebook deepfake database,
which was provided in the Kaggle's Deepfake Detection Challenge 2020. For each
video, a simple question: "Is face of the person in the video real of fake?"
was answered on average by 19 na\"ive subjects. The results of the subjective
evaluation were compared with the performance of two different state of the art
deepfake detection methods, based on Xception and EfficientNets (B4 variant)
neural networks, which were pre-trained on two other large public databases:
the Google's subset from FaceForensics++ and the recent Celeb-DF dataset. The
evaluation demonstrates that while the human perception is very different from
the perception of a machine, both successfully but in different ways are fooled
by deepfakes. Specifically, algorithms struggle to detect those deepfake
videos, which human subjects found to be very easy to spot
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
Multi-Channel Cross Modal Detection of Synthetic Face Images
Synthetically generated face images have shown to be indistinguishable from
real images by humans and as such can lead to a lack of trust in digital
content as they can, for instance, be used to spread misinformation. Therefore,
the need to develop algorithms for detecting entirely synthetic face images is
apparent. Of interest are images generated by state-of-the-art deep
learning-based models, as these exhibit a high level of visual realism. Recent
works have demonstrated that detecting such synthetic face images under
realistic circumstances remains difficult as new and improved generative models
are proposed with rapid speed and arbitrary image post-processing can be
applied. In this work, we propose a multi-channel architecture for detecting
entirely synthetic face images which analyses information both in the frequency
and visible spectra using Cross Modal Focal Loss. We compare the proposed
architecture with several related architectures trained using Binary Cross
Entropy and show in cross-model experiments that the proposed architecture
supervised using Cross Modal Focal Loss, in general, achieves most competitive
performance
Synthetic Image Detection: Highlights from the IEEE Video and Image Processing Cup 2022 Student Competition
The Video and Image Processing (VIP) Cup is a student competition that takes
place each year at the IEEE International Conference on Image Processing. The
2022 IEEE VIP Cup asked undergraduate students to develop a system capable of
distinguishing pristine images from generated ones. The interest in this topic
stems from the incredible advances in the AI-based generation of visual data,
with tools that allows the synthesis of highly realistic images and videos.
While this opens up a large number of new opportunities, it also undermines the
trustworthiness of media content and fosters the spread of disinformation on
the internet. Recently there was strong concern about the generation of
extremely realistic images by means of editing software that includes the
recent technology on diffusion models. In this context, there is a need to
develop robust and automatic tools for synthetic image detection
Black-Box Attack against GAN-Generated Image Detector with Contrastive Perturbation
Visually realistic GAN-generated facial images raise obvious concerns on
potential misuse. Many effective forensic algorithms have been developed to
detect such synthetic images in recent years. It is significant to assess the
vulnerability of such forensic detectors against adversarial attacks. In this
paper, we propose a new black-box attack method against GAN-generated image
detectors. A novel contrastive learning strategy is adopted to train the
encoder-decoder network based anti-forensic model under a contrastive loss
function. GAN images and their simulated real counterparts are constructed as
positive and negative samples, respectively. Leveraging on the trained attack
model, imperceptible contrastive perturbation could be applied to input
synthetic images for removing GAN fingerprint to some extent. As such, existing
GAN-generated image detectors are expected to be deceived. Extensive
experimental results verify that the proposed attack effectively reduces the
accuracy of three state-of-the-art detectors on six popular GANs. High visual
quality of the attacked images is also achieved. The source code will be
available at https://github.com/ZXMMD/BAttGAND
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