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Multi-attentional Deepfake Detection [article]

Hanqing Zhao, Wenbo Zhou, Dongdong Chen, Tianyi Wei, Weiming Zhang, Nenghai Yu
<span title="2021-03-08">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we instead formulate deepfake detection as a fine-grained classification problem and propose a new multi-attentional deepfake detection network.  ...  Recently, how to detect such forgery contents has become a hot research topic and many deepfake detection methods have been proposed.  ...  We propose a multi-attentional deepfake detection framework.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2103.02406v3">arXiv:2103.02406v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/syk6womid5hhtl4c4udwwskpny">fatcat:syk6womid5hhtl4c4udwwskpny</a> </span>
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ADD: Frequency Attention and Multi-View based Knowledge Distillation to Detect Low-Quality Compressed Deepfake Images [article]

Binh M. Le, Simon S. Woo
<span title="2021-12-07">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In particular, we propose the Attention-based Deepfake detection Distiller (ADD), which consists of two novel distillations: 1) frequency attention distillation that effectively retrieves the removed high-frequency  ...  components in the student network, and 2) multi-view attention distillation that creates multiple attention vectors by slicing the teacher's and student's tensors under different views to transfer the  ...  Our Approach Our Attention-based Deepfake detection Distiller (ADD) is consisted of the following two novel distillations (See Fig. 2 ): 1) frequency attention distillation and 2) multi-view attention  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2112.03553v1">arXiv:2112.03553v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/r2dhas5ee5chto3jkmtbonb26i">fatcat:r2dhas5ee5chto3jkmtbonb26i</a> </span>
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Multi-attentional Deepfake Detection [article]

Hanqing Zhao, Wenbo Zhou, Dongdong Chen, Tianyi Wei, Weiming Zhang, Nenghai Yu
<span title="2021-03-01">2021</span>
In this paper, we instead formulate deepfake detection as a fine-grained classification problem and propose a new multi-attentional deepfake detection network.  ...  Recently, how to detect such forgery contents has become a hot research topic and many deepfake detection methods have been proposed.  ...  We propose a multi-attentional deepfake detection framework.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.48550/arxiv.2103.02406">doi:10.48550/arxiv.2103.02406</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kgo6kfxcdnbxtfpanljfgjxhfe">fatcat:kgo6kfxcdnbxtfpanljfgjxhfe</a> </span>
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DFDT: An End-to-End DeepFake Detection Framework Using Vision Transformer

Aminollah Khormali, Jiann-Shiun Yuan
<span title="2022-03-14">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/smrngspzhzce7dy6ofycrfxbim" style="color: black;">Applied Sciences</a> </i> &nbsp;
DFDT is specifically designed for deepfake detection tasks consisting of four main components: patch extraction & embedding, multi-stream transformer block, attention-based patch selection followed by  ...  DFDT's transformer layer benefits from a re-attention mechanism instead of a traditional multi-head self-attention layer.  ...  [40] proposed a multi-modal approach composed of audio and video modalities to tackle deepfake detection tasks. Furthermore, Jian et al.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/app12062953">doi:10.3390/app12062953</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6vdlyruobjdpfnriivzj3xzzy4">fatcat:6vdlyruobjdpfnriivzj3xzzy4</a> </span>
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DeepFakes Detection Based on Heart Rate Estimation: Single- and Multi-frame [chapter]

Javier Hernandez-Ortega, Ruben Tolosana, Julian Fierrez, Aythami Morales
<span title="">2022</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xpct34u67nf3hfcr6srggz4hau" style="color: black;">Advances in Computer Vision and Pattern Recognition</a> </i> &nbsp;
detect the latest DeepFake videos.  ...  AbstractThis chapter describes a DeepFake detection framework based on physiological measurement.  ...  : Single-and Multi-frame DeepFakes Detection Based on Heart Rate Estimation: Single-and Multi-frame DeepFakes Detection Based on Heart Rate Estimation: Single-and Multi-frame DeepFakes Detection  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-030-87664-7_12">doi:10.1007/978-3-030-87664-7_12</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vdbdifdcovdwflog4iqgeoz2s4">fatcat:vdbdifdcovdwflog4iqgeoz2s4</a> </span>
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M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection [article]

Junke Wang, Zuxuan Wu, Wenhao Ouyang, Xintong Han, Jingjing Chen, Ser-Nam Lim, Yu-Gang Jiang
<span title="2022-04-19">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In particular, we introduce a Multi-modal Multi-scale TRansformer (M2TR), which operates on patches of different sizes to detect local inconsistencies in images at different spatial levels.  ...  In addition, to stimulate Deepfake detection research, we introduce a high-quality Deepfake dataset, SR-DF, which consists of 4,000 DeepFake videos generated by state-of-the-art face swapping and facial  ...  MaDD [71] proposes a multi-attentional Deepfake detection framework to capture artifacts with multiple attention maps.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2104.09770v3">arXiv:2104.09770v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hskipz7oxfcdrnix6b4rdgwnai">fatcat:hskipz7oxfcdrnix6b4rdgwnai</a> </span>
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Voice-Face Homogeneity Tells Deepfake [article]

Harry Cheng and Yangyang Guo and Tianyi Wang and Qi Li and Tao Ye and Liqiang Nie
<span title="2022-04-08">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Detecting forgery videos is highly desired due to the abuse of deepfake. Existing detection approaches contribute to exploring the specific artifacts in deepfake videos and fit well on certain data.  ...  paper, we propose to perform the deepfake detection from an unexplored voice-face matching view.  ...  In contrary to these approaches utilizing the vision modality only, studies nowadays exploit the multi-modal information [11, 46] for deepfake detection [36, 50, 51] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2203.02195v2">arXiv:2203.02195v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/54xw7aodcvg2ve5vbjusssxgx4">fatcat:54xw7aodcvg2ve5vbjusssxgx4</a> </span>
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Robust Deepfake On Unrestricted Media: Generation And Detection [article]

Trung-Nghia Le and Huy H Nguyen and Junichi Yamagishi and Isao Echizen
<span title="2022-02-13">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This chapter explores the evolution of and challenges in deepfake generation and detection.  ...  It also discusses possible ways to improve the robustness of deepfake detection for a wide variety of media (e.g., in-the-wild images and videos).  ...  Several researchers have recently begun to target multi-person in-the-wild images (c.f. Fig. 4b ). Zhou et al. [53] trained an attention framework to detect face forgeries in multi-person scenes.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2202.06228v1">arXiv:2202.06228v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/a37q2lf7w5bcbekk5esmbx2goe">fatcat:a37q2lf7w5bcbekk5esmbx2goe</a> </span>
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MFF-Net: Deepfake Detection Network Based on Multi-Feature Fusion

Lei Zhao, Mingcheng Zhang, Hongwei Ding, Xiaohui Cui
<span title="2021-12-17">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4d3elkqvznfzho6ki7a35bt47u" style="color: black;">Entropy</a> </i> &nbsp;
Forged videos generated by deepfaking have been widely spread and have caused severe societal impacts, which stir up public concern about automatic deepfake detection technology.  ...  Recently, many deepfake detection methods based on forged features have been proposed. Among the popular forged features, textural features are widely used.  ...  Multi-attentional deepfake detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA, 19–25 June 2021; pp. 2185–2194. 36. Chollet, F.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/e23121692">doi:10.3390/e23121692</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34945998">pmid:34945998</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8700337/">pmcid:PMC8700337</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5iiz3wt3r5hy3bajmiisbq4bk4">fatcat:5iiz3wt3r5hy3bajmiisbq4bk4</a> </span>
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BZNet: Unsupervised Multi-scale Branch Zooming Network for Detecting Low-quality Deepfake Videos

Sangyup Lee, Jaeju An, Simon S. Woo
<span title="2022-04-25">2022</span> <i title="ACM"> Proceedings of the ACM Web Conference 2022 </i> &nbsp;
We propose a novel LQ DF detection architecture, multi-scale Branch Zooming Network (BZNet), which adopts an unsupervised super-resolution (SR) technique and utilizes multi-scale images for training.  ...  Such LQ DF videos are much more challenging to detect than high-quality (HQ) DF videos.  ...  Considering frequency domain has also shown comparable Deepfake detection results, such as applying an attention layer to Xception [8] to focus on high-frequency information of the image.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3485447.3512245">doi:10.1145/3485447.3512245</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pprtqq4z2zbnbecpzvfgflwxau">fatcat:pprtqq4z2zbnbecpzvfgflwxau</a> </span>
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Deepfake Caricatures: Amplifying attention to artifacts increases deepfake detection by humans and machines [article]

Camilo Fosco, Emilie Josephs, Alex Andonian, Allen Lee, Xi Wang, Aude Oliva
<span title="2022-06-02">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Here, we introduce a novel deepfake detection framework that meets both of these needs. Our approach learns to generate attention maps of video artifacts, semi-supervised on human annotations.  ...  First, they improve the accuracy and generalizability of a deepfake classifier, demonstrated across several deepfake detection datasets.  ...  Model specification We present a framework that combines self attention and human-guided attention maps to both detect deepfakes, and expose them to the human eye.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2206.00535v2">arXiv:2206.00535v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7ph6ou2vufamfnvbricxc4c6su">fatcat:7ph6ou2vufamfnvbricxc4c6su</a> </span>
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Towards Benchmarking and Evaluating Deepfake Detection [article]

Chenhao Lin, Jingyi Deng, Pengbin Hu, Chao Shen, Qian Wang, Qi Li
<span title="2022-03-04">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Deepfake detection automatically recognizes the manipulated medias through the analysis of the difference between manipulated and non-altered videos.  ...  The results along with the shared data and evaluation methodology constitute a benchmark for comparing deepfake detection approaches and measuring progress.  ...  Multiple-attention [35] is an intra-frame level knowledge-driven method, which considers deepfake detection as a fine-grained classification problem and proposes a multi-attentional deepfake detection  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2203.02115v1">arXiv:2203.02115v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/y43yivvyynenxhdrsuls5z4ae4">fatcat:y43yivvyynenxhdrsuls5z4ae4</a> </span>
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Detection of Deepfake Videos Using Long Distance Attention [article]

Wei Lu, Lingyi Liu, Junwei Luo, Xianfeng Zhao, Yicong Zhou, Jiwu Huang
<span title="2021-06-24">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
And detection of such forgery videos is much more urgent and challenging. Most existing detection methods treat the problem as a vanilla binary classification problem.  ...  With the rapid progress of deepfake techniques in recent years, facial video forgery can generate highly deceptive video contents and bring severe security threats.  ...  Index Terms-Deepfake detection, face manipulation, attention mechanism, spatial and temporal artifacts. I.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2106.12832v1">arXiv:2106.12832v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tox7l7dwubhdzpgaliejouthmi">fatcat:tox7l7dwubhdzpgaliejouthmi</a> </span>
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Spotting DeepFakes and Face Manipulations by Fusing Features from Multi-Stream CNNs Models

Semih Yavuzkilic, Abdulkadir Sengur, Zahid Akhtar, Kamran Siddique
<span title="2021-07-26">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/nzoj5rayr5hutlurimhzyjlory" style="color: black;">Symmetry</a> </i> &nbsp;
Deepfake manipulations may be done with a variety of techniques and applications. A quintessential countermeasure against deepfake or face manipulation is deepfake detection method.  ...  In this paper, a new large-scale dataset (i.e., World Politicians Deepfake Dataset (WPDD)) is introduced to improve deepfake detection systems.  ...  [29] Convolutional Attention Network (CAN) Celeb-DF and DFDC 2021 This paper presents a new large-scale dataset (i.e., World Politicians Deepfake Dataset (WPDD)) to improve deepfake detection algorithms  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/sym13081352">doi:10.3390/sym13081352</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/46vkttmlx5dufkh267uveyrqoa">fatcat:46vkttmlx5dufkh267uveyrqoa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210803225537/https://res.mdpi.com/d_attachment/symmetry/symmetry-13-01352/article_deploy/symmetry-13-01352-v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/dc/0b/dc0b220856c8c38495e81ded8781fa25b2ecf266.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/sym13081352"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

Block shuffling learning for Deepfake Detection [article]

Sitong Liu, Zhichao Lian, Siqi Gu, Liang Xiao
<span title="2022-02-06">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Although the deepfake detection based on convolutional neural network has achieved good results, the detection results show that these detectors show obvious performance degradation when the input images  ...  Extensive experiments show that our proposed method achieves state-of-the-art performance in forgery face detection, including good generalization ability in the face of common image transformations.  ...  [38] propose a multi-attentional network to focus on different local parts and subtle artifacts. Wang et al.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2202.02819v1">arXiv:2202.02819v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/klun7hz5njfvtkg6fwdyqcsovm">fatcat:klun7hz5njfvtkg6fwdyqcsovm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220209121350/https://arxiv.org/pdf/2202.02819v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/93/6e/936eed579a95e95dddaf9a6ffa03e52ae9abe3a1.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2202.02819v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>
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