Face Swapping
192 papers with code • 2 benchmarks • 9 datasets
Face swapping refers to the task of swapping faces between images or in an video, while maintaining the rest of the body and environment context.
( Image credit: Swapped Face Detection using Deep Learning and Subjective Assessment )
Benchmarks
These leaderboards are used to track progress in Face Swapping
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Libraries
Use these libraries to find Face Swapping models and implementationsDatasets
FaceForensics++
Celeb-DF
AFLW2000-3D
DFDC
WildDeepfake
DeeperForensics-1.0
VideoForensicsHQ
DFDM
VGGFace2 HQ
Latest papers with no code
Deepfakes and Higher Education: A Research Agenda and Scoping Review of Synthetic Media
The availability of software which can produce convincing yet synthetic media poses both threats and benefits to tertiary education globally.
Retrieval-Augmented Audio Deepfake Detection
With recent advances in speech synthesis including text-to-speech (TTS) and voice conversion (VC) systems enabling the generation of ultra-realistic audio deepfakes, there is growing concern about their potential misuse.
Texture-aware and Shape-guided Transformer for Sequential DeepFake Detection
In this paper, we propose a novel Texture-aware and Shape-guided Transformer to enhance detection performance.
FreqBlender: Enhancing DeepFake Detection by Blending Frequency Knowledge
Existing methods typically generate these faces by blending real or fake faces in color space.
Explainable Deepfake Video Detection using Convolutional Neural Network and CapsuleNet
Deepfake technology, derived from deep learning, seamlessly inserts individuals into digital media, irrespective of their actual participation.
DeepFake-O-Meter v2.0: An Open Platform for DeepFake Detection
Furthermore, it serves as an evaluation and benchmarking platform for researchers in digital media forensics to compare the performance of multiple algorithms on the same input.
DUPE: Detection Undermining via Prompt Engineering for Deepfake Text
Yet the accuracy of many of these detectors has not been thoroughly verified, posing potential harm to students who are falsely accused of academic dishonesty.
Towards More General Video-based Deepfake Detection through Facial Feature Guided Adaptation for Foundation Model
With the rise of deep learning, generative models have enabled the creation of highly realistic synthetic images, presenting challenges due to their potential misuse.
Cross-Domain Audio Deepfake Detection: Dataset and Analysis
Audio deepfake detection (ADD) is essential for preventing the misuse of synthetic voices that may infringe on personal rights and privacy.
D$^3$: Scaling Up Deepfake Detection by Learning from Discrepancy
The boom of Generative AI brings opportunities entangled with risks and concerns.
