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https://github.com/LightningChan/awesome_face_forensics

Resources for face forgery detection
https://github.com/LightningChan/awesome_face_forensics

List: awesome_face_forensics

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Resources for face forgery detection

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# awesome_face_forensics

Resources for face forensics



### Survey

- [2022-01-图像图形学报] 视觉深度伪造检测技术综述 [`paper`](http://www.cjig.cn/jig/ch/reader/view_abstract.aspx?file_no=20220104&flag=1)

- [2021-12-信号处理] 人脸视频深度伪造与防御技术综述 [`paper`](http://www.signal.org.cn/CN/article/downloadArticleFile.do?attachType=PDF&id=21206)

- [2021-09] DeepFakes: Detecting Forged and Synthetic Media Content Using Machine Learning [`paper`](https://arxiv.org/abs/2109.02874)

- [2021-09] Challenges and Solutions in DeepFakes [`paper`](https://arxiv.org/abs/2109.05397)

- [2021-07] MMSys'21 Grand Challenge on Detecting Cheapfakes [`paper`](https://arxiv.org/abs/2107.05297)

- [2021-06] A survey on face forgery detection of Deepfake [`paper`](https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11878/118780J/A-survey-on-face-forgery-detection-of-Deepfake/10.1117/12.2600889.short?SSO=1)

- [2021-05] Deep Insights of Deepfake Technology : A Review [`paper`](https://arxiv.org/abs/2105.00192)

- [2021-03] Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward [`paper`](https://arxiv.org/abs/2103.00484) [Nice!]

- [2021-03] Countering Malicious DeepFakes: Survey, Battleground, and Horizon [`paper`](https://arxiv.org/abs/2103.00218) [Nice!] [`page`](http://www.xujuefei.com/dfsurvey)

- [2020-12] The Emerging Threats of Deepfake Attacks and Countermeasures [`paper`](https://arxiv.org/abs/2012.07989)

- [2020-10] A Survey of Machine Learning Techniques in Adversarial Image Forensics [`paper`](https://arxiv.org/abs/2010.09680) [Nice!]

- [2020-05] 深度伪造与检测技术综述 [`paper`](http://www.jos.org.cn/jos/ch/reader/view_abstract.aspx?flag=1&file_no=6140&journal_id=jos)

- [2020-03] A Survey of Deep Learning-Based Source Image Forensics [`paper`](https://www.mdpi.com/2313-433X/6/3/9)

- [2020-09] Deepfake detection: humans vs. machines [`paper`](https://arxiv.org/abs/2009.03155)

- [2020-04] The Creation and Detection of Deepfakes: A Survey [`paper`](https://arxiv.org/abs/2004.11138)

- [2020-03] DeepFake Detection: Current Challenges and Next Steps [`paper`](https://arxiv.org/abs/2003.09234)

- [2020-01] Media Forensics and DeepFakes: an overview [`paper`](https://arxiv.org/abs/2001.06564)

- [2020-01] DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection [`paper`](https://arxiv.org/abs/2001.00179)

- [2019-09] Deep Learning for Deepfakes Creation and Detection [`paper`](https://arxiv.org/abs/1909.11573)

- [2019-07] Face Authenticity: An Overview of Face Manipulation Generation, Detection and Recognition [`paper`](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3419272)

- [2018-11] Facial re-enactment, speech synthesis and the rise of the Deepfake [`paper`](https://ro.ecu.edu.au/cgi/viewcontent.cgi?article=2530&context=theses_hons)



### Datasets

- [2022-05] A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials [`paper`](https://arxiv.org/abs/2205.05467)

- [2022-03] Towards Benchmarking and Evaluating Deepfake Detection [`paper`](https://arxiv.org/abs/2203.02115)

- [2021-08] FakeAVCeleb: A Novel Audio-Video Multimodal Deepfake Dataset [`paper1`](https://arxiv.org/abs/2108.05080) [`paper2`](https://arxiv.org/abs/2109.02993) [`code`](https://github.com/hasam6400/fakevaceleb)

- [2021-08] BioFors: A Large Biomedical Image Forensics Dataset [`paper`](https://arxiv.org/abs/2108.12961)

- [2021-08] DeepFake MNIST+: A DeepFake Facial Animation Dataset [`paper`](https://arxiv.org/abs/2108.07949)

- [2021-07-ICCV] OpenForensics: Large-Scale Challenging Dataset For Multi-Face Forgery Detection And Segmentation In-The-Wild [`paper`](https://arxiv.org/abs/2107.14480)

- [2021-06-IJBC] DFGC 2021: A DeepFake Game Competition [`paper`](https://arxiv.org/abs/2106.01217)

- [2021-03-WWW] Deepfake Videos in the Wild: Analysis and Detection [`paper`](https://arxiv.org/abs/2103.04263)

- [2021-03-ICCV] KoDF: A Large-scale Korean DeepFake Detection Dataset [`paper`](https://arxiv.org/abs/2103.10094)

- [2021-03-CVPR] Face Forensics in the Wild [`paper`](https://arxiv.org/abs/2103.16076) [`code`](https://github.com/tfzhou/FFIW)

- [2021-12-CVPR] ForgeryNet -- Face Forgery Analysis Challenge 2021: Methods and Results [`paper`](https://arxiv.org/abs/2112.08325)

- [2021-03-CVPR] ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis [`paper`](https://arxiv.org/abs/2103.05630)

- [2021-02] DeeperForensics Challenge 2020 on Real-World Face Forgery Detection: Methods and Results [`paper`](https://arxiv.org/abs/2102.09471)

- [2020-12] Identity-Driven DeepFake Detection [`paper`](https://arxiv.org/abs/2012.03930)

- [2020-MM] WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection [`paper`](https://dl.acm.org/doi/10.1145/3394171.3413769) [`github`](https://github.com/deepfakeinthewild/deepfake-in-the-wild)

- [2020-05-ICME] VideoForensicsHQ: Detecting High-quality Manipulated Face Videos [`paper`](https://arxiv.org/abs/2005.10360) [`page`](http://gvv.mpi-inf.mpg.de/projects/VForensicsHQ/)

- [x] [2020-01-CVPR] DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection [`paper`](https://arxiv.org/abs/2001.03024)

- [x] [2020-06] The DeepFake Detection Challenge (DFDC) Dataset [`paper`](https://arxiv.org/abs/2006.07397)

- [x] [2019-10-ICMEW] The Deepfake Detection Challenge (DFDC) Preview Dataset [`paper`](https://arxiv.org/abs/1910.08854)

- [x] [2019-09-CVPR] Celeb-DF: A New Dataset for DeepFake Forensics [`paper`](https://arxiv.org/abs/1909.12962)

- [x] [2019-09] Swapped Face Detection using Deep Learning and Subjective Assessment [`paper`](https://arxiv.org/abs/1909.04217)

- [x] [2019-01-ICCV] FaceForensics++: Learning to Detect Manipulated Facial Images [`paper`](https://arxiv.org/abs/1901.08971) [`code`](https://github.com/ondyari/FaceForensics)

- [x] [2018-12] DeepFakes: a New Threat to Face Recognition? Assessment and Detection [`paper1`](https://arxiv.org/abs/1812.08685) [`paper2`](https://ieeexplore.ieee.org/document/8987375)

- [x] [2018] Fake face detection methods: Can they be generalized? [`paper`](https://ieeexplore.ieee.org/document/8553251) [`page`](http://ali.khodabakhsh.org/research/ffw/)

- [x] [2018-03-WIFS] FaceForensics: A Large-scale Video Dataset for Forgery Detection in Human Faces [`paper`](https://arxiv.org/abs/1803.09179)



### Method



#### 2022

- Few-shot Forgery Detection via Guided Adversarial Interpolation (ArXiv) [`paper`](https://arxiv.org/abs/2204.05905)

- Audio-Visual Person-of-Interest DeepFake Detection (ArXiv) [`paper`](https://arxiv.org/abs/2204.03083) [`code`](https://github.com/grip-unina/poi-forensics)

- Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection (CVPR) [`paper`](https://arxiv.org/abs/2203.12208) [`code`](https://github.com/liangchen527/SLADD)

- Exploring Frequency Adversarial Attacks for Face Forgery Detection (CVPR) [`paper`](https://arxiv.org/abs/2203.15674)

- End-to-End Reconstruction-Classification Learning for Face Forgery Detection (CVPR)

- Self-supervised Transformer for Deepfake Detection (CVPR) [`paper`](https://arxiv.org/abs/2203.01265)

- Protecting Celebrities with Identity Consistency Transformer（CVPR） [`paper`](https://arxiv.org/abs/2203.01318)

- Leveraging Real Talking Faces via Self-Supervision for Robust Forgery Detection [`paper`](https://arxiv.org/abs/2201.07131)

- Delving into the Local: Dynamic Inconsistency Learning for DeepFake Video Detection (AAAI) [`paper`](https://www.aaai.org/AAAI22Papers/AAAI-1978.GuZ.pdf)

- Exploiting Fine-grained Face Forgery Clues via Progressive Enhancement Learning (AAAI) [`paper`](https://arxiv.org/abs/2112.13977)

- Dual Contrastive Learning for General Face Forgery Detection (AAAI) [`paper`](https://arxiv.org/abs/2112.13522)

- ADD: Frequency Attention and Multi-View based Knowledge Distillation to Detect Low-Quality Compressed Deepfake Images (AAAI) [`paper`](https://arxiv.org/abs/2112.03553) [`code`](https://github.com/Leminhbinh0209/ADD)

- CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes (AAAI) [`paper`](https://arxiv.org/abs/2105.10872)

- Responsible Disclosure of Generative Models Using Scalable Fingerprinting (ICLR) [`paper`](https://openreview.net/pdf?id=sOK-zS6WHB)



#### 2021

- Spatiotemporal Inconsistency Learning for DeepFake Video Detection (MM) [`paper`](https://arxiv.org/abs/2109.01860)

- Video Transformer for Deepfake Detection with Incremental Learning (MM) [`paper`](https://arxiv.org/abs/2108.05307)

- Metric Learning for Anti-Compression Facial Forgery Detection (MM) [`paper`](https://arxiv.org/abs/2103.08397)

- Video Transformer for Deepfake Detection with Incremental Learning (MM) [`paper`](https://arxiv.org/abs/2108.05307)

- CoReD: Generalizing Fake Media Detection with Continual Representation using Distillation (MM) [`paper`](https://arxiv.org/abs/2107.02408)

- FakeTagger: Robust Safeguards against DeepFake Dissemination via Provenance Tracking (MM)[`paper`](https://dl.acm.org/doi/pdf/10.1145/3474085.3475518)

- Improving the Efficiency and Robustness of Deepfakes Detection through Precise Geometric Features (CVPR) [`paper`](https://arxiv.org/abs/2104.04480) [`code`](https://github.com/frederickszk/LRNet)

- Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection (CVPR) [`paper`](https://arxiv.org/abs/2012.07657) 

- Multi-attentional Deepfake Detection (CVPR)  [`paper`](https://arxiv.org/abs/2103.02406) [`code`](https://github.com/yoctta/multiple-attention)

- Face Forgery Detection by 3D Decomposition (CVPR)  [`paper`](https://arxiv.org/abs/2011.09737)

- Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain (CVPR) [`paper`](https://arxiv.org/abs/2103.01856)

- Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection (CVPR) [`paper`](https://arxiv.org/abs/2103.09096)[`Sup`](https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Frequency-Aware_Discriminative_Feature_CVPR_2021_supplemental.pdf)

- Generalizing Face Forgery Detection with High-frequency Features (CVPR) [`paper`](https://arxiv.org/abs/2103.12376)

- A Closer Look at Fourier Spectrum Discrepancies for CNN-generated Images Detection (CVPR) [`paper`](https://arxiv.org/abs/2103.17195) [`code`](https://github.com/sutd-visual-computing-group/Fourier-Discrepancies-CNN-Detection/)

- Representative Forgery Mining for Fake Face Detection (CVPR) [`paper`](https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Representative_Forgery_Mining_for_Fake_Face_Detection_CVPR_2021_paper.pdf)

- MagDR: Mask-Guided Detection and Reconstruction for Defending Deepfakes (CVPR) [`paper`](https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_MagDR_Mask-Guided_Detection_and_Reconstruction_for_Defending_Deepfakes_CVPR_2021_paper.pdf)

- Detecting Deepfake Videos with Temporal Dropout 3DCNN（IJCAI）[`paper`](https://www.ijcai.org/proceedings/2021/178)

- Dynamic Inconsistency-aware DeepFake Video Detection（IJCAI） [`paper`](https://www.ijcai.org/proceedings/2021/102)

- Beyond the Spectrum: Detecting Deepfakes via Re-Synthesis（IJCAI） [`paper`](https://arxiv.org/abs/2105.14376)

- An Examination of Fairness of AI Models for Deepfake Detection（IJCAI） [`paper`](https://arxiv.org/abs/2105.00558)

- Towards Solving the DeepFake Problem : An Analysis on Improving DeepFake Detection using Dynamic Face Augmentation (ICCV) [`paper`](https://arxiv.org/abs/2102.09603)

- Learning Self-Consistency for Deepfake Detection (ICCV) [`paper`](https://arxiv.org/abs/2012.09311)

- Joint Audio-Visual Deepfake Detection (ICCV) [`paper`](https://openaccess.thecvf.com/content/ICCV2021/papers/Zhou_Joint_Audio-Visual_Deepfake_Detection_ICCV_2021_paper.pdf)

-  Exploring Temporal Coherence for More General Video Face Forgery (ICCV) [`paper`](https://arxiv.org/abs/2108.06693)

-  ID-Reveal: Identity-aware DeepFake Video Detection (ICCV) [`paper`](https://arxiv.org/abs/2012.02512)

-  Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data (ICCV) [`paper`](https://arxiv.org/abs/2007.08457)

-  Local Relation Learning for Face Forgery Detection (AAAI) [`paper`](https://www.aaai.org/AAAI21Papers/AAAI-1964.ChenS.pdf)

-  Domain General Face Forgery Detection by Learning to Weight (AAAI) [`paper`](https://www.aaai.org/AAAI21Papers/AAAI-589.SunK.pdf)

- Generalized Facial Manipulation Detection with Edge Region Feature Extraction (WACV) [`paper`](https://arxiv.org/abs/2102.01381)

- Finding Facial Forgery Artifacts with Parts-Based Detectors (WACV)  [`paper`](https://arxiv.org/abs/2109.10688)

- DLFMNet: End-to-End Detection and Localization of Face Manipulation Using Multi-Domain Features  (ICME) [`paper`](https://ieeexplore.ieee.org/abstract/document/9428450/)

- DefakeHop: A Light-Weight High-Performance Deepfake Detector (ICME) [`paper`](https://arxiv.org/abs/2103.06929)

- Visual-Semantic Transformer for Face Forgery Detection (IJCB) [`paper`](https://ieeexplore.ieee.org/document/9484407)

- DeepFake Detection Based on Discrepancies Between Faces and their Context (TPAMI) [`paper`](https://ieeexplore.ieee.org/document/9468380/)

- FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals (TPAMI) [`paper`](https://ieeexplore.ieee.org/document/9141516/)

- Detection of Fake and Fraudulent Faces via Neural Memory Networks (TIFS) [`paper`](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9309253)

- Preventing DeepFake Attacks on Speaker Authentication by Dynamic Lip Movement Analysis  (TIFS)[`paper`](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9298826)



#### 2020

- Sharp Multiple Instance Learning for DeepFake Video Detection [`paper`](https://arxiv.org/abs/2008.04585) (MM)

- DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms [`paper`](https://arxiv.org/abs/2006.07634) (MM)

- Emotions Don't Lie: A Deepfake Detection Method using Audio-Visual Affective Cues [`paper`](https://arxiv.org/abs/2003.06711) (MM)

- DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms (MM) [`paper`](https://arxiv.org/abs/2006.07634) 

- Not made for each other- Audio-Visual Dissonance-based Deepfake Detection and Localization (MM)[`paper`](https://arxiv.org/abs/2005.14405)

- Face X-ray for More General Face Forgery Detection (CVPR) [`paper`](https://arxiv.org/abs/1912.13458)

- On the Detection of Digital Face Manipulation (CVPR) [`paper`](https://arxiv.org/abs/1910.01717) [`code`](https://github.com/JStehouwer/FFD_CVPR2020)

- Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributions (CVPR) [`paper`](https://arxiv.org/abs/2003.01826) [`code`](https://github.com/cc-hpc-itwm/UpConv)

- Global Texture Enhancement for Fake Face Detection in the Wild (CVPR) [`paper`](https://openaccess.thecvf.com/content_CVPR_2020/papers/Liu_Global_Texture_Enhancement_for_Fake_Face_Detection_in_the_Wild_CVPR_2020_paper.pdf)

- FakeSpotter: A Simple yet Robust Baseline for Spotting AI-Synthesized Fake Faces (IJCAI) [`paper`](https://www.ijcai.org/Proceedings/2020/0476.pdf)

- Two-branch Recurrent Network for Isolating Deepfakes in Videos (ECCV) [`paper`](https://arxiv.org/abs/2008.03412)

- Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Clues (ECCV)[`paper`](https://arxiv.org/abs/2007.09355)

- What makes fake images detectable? Understanding properties that generalize (ECCV)[`paper`](https://arxiv.org/abs/2008.10588)

- Leveraging Frequency Analysis for Deep Fake Image Recognition (ICML) [`paper`](https://arxiv.org/abs/2003.08685)

- FakeLocator: Robust Localization of GAN-Based Face Manipulations (IFS) [`paper`](https://arxiv.org/abs/2001.09598) 

- Fourier Spectrum Discrepancies in Deep Network Generated Images (NeurIPS) [`paper`](https://arxiv.org/abs/1911.06465) 

- AOT: Appearance Optimal Transport Based Identity Swapping for Forgery Detection  (NeurIPS) [`paper`](https://papers.nips.cc/paper/2020/file/f718499c1c8cef6730f9fd03c8125cab-Paper.pdf)

- Interpretable Deepfake Detection via Dynamic Prototypes (WACV) [`paper`](https://arxiv.org/abs/2006.15473)

- FSSPOTTER: Spotting Face-Swapped Video by Spatial and Temporal Clues (ICME) [`paper`](https://ieeexplore.ieee.org/document/9102914)



#### 2019



-  Detecting and Simulating Artifacts in GAN Fake Images (WIFS) [`paper`](https://arxiv.org/abs/1907.06515) [`code`](https://github.com/ColumbiaDVMM/AutoGAN)

- Exploiting Visual Artifacts to Expose Deepfakes and Face Manipulations (WACVW) [`paper`](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8638330)

- Protecting World Leaders Against Deep Fakes (CVPRW) [`paper`](https://openaccess.thecvf.com/content_CVPRW_2019/papers/Media%20Forensics/Agarwal_Protecting_World_Leaders_Against_Deep_Fakes_CVPRW_2019_paper.pdf)

- Towards Generalizable Forgery Detection with Locality-aware AutoEncoder (CIKM) [`paper`](https://arxiv.org/abs/1909.05999) 



#### 2018

-  Exposing Deep Fakes Using Inconsistent Head Poses (ICASSP) [`paper`](https://arxiv.org/abs/1811.00661)

-  Mesonet: a compact facial video forgery detection network (WIFS) [`paper`](https://arxiv.org/abs/1809.00888) [`code`](https://github.com/DariusAf/MesoNet) [`dataset`](https://my.pcloud.com/publink/show?code=XZLGvd7ZI9LjgIy7iOLzXBG5RNJzGFQzhTRy) [`pytorch`](https://github.com/HongguLiu/MesoNet.Pytorch)

-  Exposing DeepFake Videos By Detecting Face Warping Artifacts (CVPRW) [`paper`](https://arxiv.org/abs/1811.00656) [`code`](https://github.com/danmohaha/CVPRW2019_Face_Artifacts)

-  Two-stream neural networks for tampered face detection (CVPRW) [`paper`](https://arxiv.org/abs/1803.11276)