En los últimos años se ha visto un crecimiento exponencial de los desarrollos orientados a la creación de contenido textual, gráfico, sonoro y audiovisual mediante inteligencia artificial. Son ...logros tecnológicos extraordinarios que ofrecen grandes oportunidades potenciales, pero a la vez quedan expuestos a ciertos usos cuestionables que son, en sà mismos, amenazas. Este artÃculo examina iniciativas recientes dirigidas a la simulación de la escritura humana, la creación de vÃdeos deepfake, la clonación de voz y la generación de imágenes a partir de indicaciones textuales. De estos desarrollos se comentan los beneficios potenciales y los principales peligros derivados de un uso incorrecto.
The current high-fidelity generation and high-precision detection of DeepFake images are at an arms race. We believe that producing DeepFakes that are highly realistic and "detection evasive" can ...serve the ultimate goal of improving future generation DeepFake detection capabilities. In this paper, we propose a simple yet powerful pipeline to reduce the artifact patterns of fake images without hurting image quality by performing implicit spatial-domain notch filtering. We first demonstrate that frequency-domain notch filtering, although famously shown to be effective in removing periodic noise in the spatial domain, is infeasible for our task at hand due to the manual designs required for the notch filters. We, therefore, resort to a learning-based approach to reproduce the notch filtering effects, but solely in the spatial domain. We adopt a combination of adding overwhelming spatial noise for breaking the periodic noise pattern and deep image filtering to reconstruct the noise-free fake images, and we name our method DeepNotch. Deep image filtering provides a specialized filter for each pixel in the noisy image, producing filtered images with high fidelity compared to their DeepFake counterparts. Moreover, we also use the semantic information of the image to generate an adversarial guidance map to add noise intelligently. Our large-scale evaluation on 3 representative DeepFake detection methods (tested on 16 types of DeepFakes) has demonstrated that our technique significantly reduces the accuracy of these 3 fake image detection methods, 36.79% on average and up to 97.02% in the best case.
A deepfake is a video, audio or other content (e.g. image) that is completely or partially fabricated or created by manipulating existing, real content. Just as fake news calls into question the ...authenticity of real news, deepfake also calls into question the authenticity of real content. At the same time, deepfake has many advantages in addition to its often mentioned dangers. Following a historical overview of deepfake, the study describes these benefits and dangers, and then discusses possible legal responses after presenting tools for detecting deepfake.
A deepfake olyan videó, hang vagy egyéb tartalom (például kép), amely teljesen vagy részben kitalált, vagy egy meglévő, valós tartalom manipulálásával jött létre. Ahogyan az álhírek (fake news) megkérdőjelezik a valós hírek hitelességét, a mélyhamisítás (deepfake) is megkérdőjelezi a valós tartalmak valódiságát. Ugyanakkor a deepfake-nek a sokszor hangoztatott veszélyei mellett számos előnye is van. A tanulmány a deepfake történeti áttekintését követően ezeket az előnyöket és veszélyeket ismerteti, majd a deepfake észlelésére szolgáló eszközök bemutatását követően kitér a lehetséges jogi válaszlépésekre.
Audio-visual deepfake detection is the process of identifying and detecting deepfakes that have been generated using both audio and visual content with AI algorithms. Most existing methods primarily ...focus on the overall authenticity while neglecting the position of forgeries in time. This can be particularly problematic, as even a small alteration in a clip can significantly impact its meaning. Such brand new attacks are dangerous and how to tackle such attacks remains an open question. In this paper, we present a novel neural network-based model to tackle the temporal forgery detection (TFD) problem. It consists of new audio and visual encoders with cross-modal attention for embedding extraction, and an embedding-level fusion mechanism with self-attention for forgery localization. Besides, a multi-dimensional contrastive loss is proposed which helps the model not only to capture audio-visual inconsistency for deepfake detection but also to exploit temporal inconsistency by coherently constraining the extracted embeddings. Extensive experiments on the LAV-DF dataset show that the presented method outperforms several state-of-the-art temporal forgery localization methods by up to 23.4% on AP@0.5 and 13.8% on AR@100. In addition, we also show the effectiveness of the proposed model on deepfake detection.
Advancements in computer vision and deep learning have led to difficulty in distinguishing Deepfake and real videos. In particular, forgery audios are also generated to accompany fake videos and make ...them more realistic, which makes Deepfake detection more difficult. Existing Deepfake detection methods that use multimodal information ignore the representation gap between different modalities, resulting in limited performance. To address this problem, in this paper, a novel Deepfake detection method utilizing multimodal contrastive learning (MCL) is proposed to better explore intra-modal and cross-modal forgery clues. To reduce the cross-modal gap and explore multimodal forgery artifacts, a cross-modal contrastive learning strategy is designed to learn a compositional embedding from multimodal information, which facilitates pulling together representations across uni-modalities and multi-modalities. Moreover, to supplement the intra-frame forgery clues mining ability of the video network, the frame knowledge is distilled to the video network without adding additional computation. Specifically, to mine intra-modal clues, three modality features are first extracted from audio, frame and video, respectively. Secondly, the audio and frame features are separately composed with the video feature to derive two cross-modal representations. Subsequently, these cross-modal features are contrastive with the intra-modal features to reduce cross-modal gap. By jointly pulling together the unimodal and multimodal features through MCL, a more effective representation that contains intra-modal and cross-modal forgery artifacts can be learned. Finally, a noise-based feature augmentation (NFA) module is proposed to adaptively perturb the audio-visual feature and further improve generalization performance. Extensive experiments demonstrate that the proposed framework outperforms SOTA methods.
Deepfake videos are becoming more pervasive. In this preregistered online experiment, participants (N = 454, Mage = 37.19, SDage = 13.25, males = 57.5%) categorize a series of 20 videos as either ...real or deepfake. All participants saw 10 real and 10 deepfake videos. Participants were randomly assigned to receive a list of strategies for detecting deepfakes based on visual cues (e.g., looking for common artifacts such as skin smoothness) or to act as a control group. Participants were also asked how confident they were that they categorized each video correctly (per video confidence) and to estimate how many videos they correctly categorized out of 20 (overall confidence). The sample performed above chance on the detection activity, correctly categorizing 60.70% of videos on average (SD = 13.00). The detection strategies intervention did not impact detection accuracy or detection confidence, with the intervention and control groups performing similarly on the detection activity and showing similar levels of confidence. Inconsistent with previous research, the study did not find that participants had a bias toward categorizing videos as real. Participants overestimated their ability to detect deepfakes at the individual video level. However, they tended to underestimate their abilities on the overall confidence question.
•Participants categorized 20 videos as real or deepfake (half were deepfake).•Mean categorization accuracy was 60.7%.•Providing detection strategies did not impact participant accuracy or confidence.•Participants were overconfident at the individual video level.•Participants were underconfident when rating their overall performance.
To make deepfake detectors generalizable to different target domains, one effective way is to let the source domain for training be similar to the target domain for detection. This letter tackles the ...problem from the perspective of domain adaptation and achieves both image-level and feature-level domains alignments. The proposed unsupervised domain adapter accomplishes the image-level domain alignment relying on the combination of cross-domain style feature mixing and diffusion model, and the feature-level domain alignment relying on prototypical consistency guided supervision and adversarial learning. The style is transferred from source to target for the generation of target data proxy in the form of stylized images. The features of the stylized images are further aligned with the target prototypical features. We apply the domain adapter as a feature booster to four current deepfake detectors. Experimental results show that all the detectors get a significant increase in AUC values on cross-dataset testings. We further propose a deepfake detector based on the Xception backbone with our booster. Compared with five state-of-the-art detectors, the proposed detector performs best in all experiments.
Although the recent advancement in generative models brings diverse advantages to society, it can also be abused with malicious purposes, such as fraud, defamation, and fake news. To prevent such ...cases, vigorous research is conducted to distinguish the generated images from the real images, but challenges still remain to distinguish the generated images outside of the training settings. Such limitations occur due to data dependency arising from the model’s overfitting issue to the specific Generative Adversarial Networks (GANs) and categories of the training data. To overcome this issue, we adopt a self-supervised scheme. Our method is composed of the artificial artifact generator reconstructing the high-quality artificial artifacts of GAN images, and the GAN detector distinguishing GAN images by learning the reconstructed artificial artifacts. To improve the generalization of the artificial artifact generator, we build multiple autoencoders with different numbers of upconvolution layers. With numerous ablation studies, the robust generalization of our method is validated by outperforming the generalization of the previous state-of-the-art algorithms, even without utilizing the GAN images of the training dataset.
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•A novel framework to train a GAN detector in the self-supervision scheme.•New architecture employing multiple autoencoders to reproduce the fingerprints of GANs.•Outstanding robustness to unknown GANs compared to the supervised GAN detectors.•Impressive performance for zero-shot and few-shot transfer learning.•Detailed analysis of the proposed framework with numerous ablation tests.
The existing face forgery algorithms have achieved remarkable progress in how to generate reasonable facial images and can even successfully deceive human beings. Considering public security, face ...forgery detection is of vital importance, making it essential to design face forgery detection algorithms to detect forgery images over the Internet. Despite the great success achieved by the existing Deepfake detection algorithms, they usually failed to achieve satisfactory Deepfake detection performance when deployed to handle the forgery videos in practice. One significant reason is compression. The videos over the Internet are inevitably compressed considering the transmission efficiency. The video compression results in significant Deepfake detection performance degradation for the existing Deepfake detection algorithms. To address this issue, in this article, we propose a generic, simple yet effective "bleaching" pre-processing module based on the generative model and the high-level feature representations to produce a bleached image, which shares a similar appearance with the compressed images. The bleached images with recovered information can be identified accurately by the optimized Deepfake detection models without retraining. The proposed method has utilized a redesigned feature representation, which serves as a navigator to effectively and sufficiently alter the feature distribution in the high-dimensional space to remedy the difference between real facial images and forgery counterparts. Thus, the proposed method can successfully avoid misclassification. Comprehensive and extensive experiments are carried out on four low-quality Faceforensics++ datasets, demonstrating the effectiveness of our method in recovering the information loss caused by the compression artifacts across various backbones and compression.
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