A Survey of Deep Fake Detection for Trial Courts
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Abstract (arXiv)
Recently, image manipulation has achieved rapid growth due to the advancement of sophisticated image editing tools. A recent surge of generated fake imagery and videos using neural networks is DeepFake. DeepFake algorithms can create fake images and videos that humans cannot distinguish from authentic ones. (GANs) have been extensively used for creating realistic images without accessing the original images. Therefore, it is become essential to detect fake videos to avoid spreading false information. This paper presents a survey of methods used to detect DeepFakes and datasets available for detecting DeepFakes in the literature to date. We present extensive discussions and research trends related to DeepFake technologies.
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References · 9
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
- 2019FaceForensics++: Learning to Detect Manipulated Facial Imagescited by 1,101
- 2018Exposing Deep Fakes Using Inconsistent Head Posescited by 297
- 2018DeepFakes: a New Threat to Face Recognition? Assessment and Detectioncited by 209
- 2019The Deepfake Detection Challenge (DFDC) Preview Datasetcited by 209
- 2018Detecting GAN-generated Imagery using Color Cuescited by 76
- 2017Exploring Neural Transducers for End-to-End Speech Recognitioncited by 44
- 2015Robust Face Recognition via Multimodal Deep Face Representationcited by 19
- 2017Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs)cited by 18
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