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Found 6 papers, 2 papers with code
Exploiting Modality-Specific Features For Multi-Modal Manipulation Detection And Grounding
Existing methods for multi-modal manipulation detection and grounding primarily focus on fusing vision-language features to make predictions, while overlooking the importance of modality-specific features, leading to sub-optimal results.
Multi-spectral Class Center Network for Face Manipulation Detection and Localization
Next, a novel Multi-Spectral Class Center Network (MSCCNet) is proposed for face manipulation detection and localization.
UIA-ViT: Unsupervised Inconsistency-Aware Method based on Vision Transformer for Face Forgery Detection
UPCL is designed for learning the consistency-related representation with progressive optimized pseudo annotations.
Towards Intrinsic Common Discriminative Features Learning for Face Forgery Detection using Adversarial Learning
Existing face forgery detection methods usually treat face forgery detection as a binary classification problem and adopt deep convolution neural networks to learn discriminative features.
DFGC 2021: A DeepFake Game Competition
This competition provides a common platform for benchmarking the adversarial game between current state-of-the-art DeepFake creation and detection methods.
Towards Generalizable and Robust Face Manipulation Detection via Bag-of-local-feature
Over the past several years, in order to solve the problem of malicious abuse of facial manipulation technology, face manipulation detection technology has obtained considerable attention and achieved remarkable progress.
