Papers Temporal Forgery Localization
“Temporal Forgery Localization” 태그가 달린 논문 9편 · 필터 해제
Context-aware TFL: A Universal Context-aware Contrastive Learning Framework for Temporal Forgery Localization
Most research efforts in the multimedia forensics domain have focused on detecting forgery audio-visual content and reached sound achievements. However, these works only consider deepfake detection as a classification ta…
Anomaly DetectionContrastive LearningDeepFake DetectionFace Swapping+1Weakly-supervised Audio Temporal Forgery Localization via Progressive Audio-language Co-learning Network
Audio temporal forgery localization (ATFL) aims to find the precise forgery regions of the partial spoof audio that is purposefully modified. Existing ATFL methods rely on training efficient networks using fine-grained a…
Contrastive LearningTemporal Forgery LocalizationDiMoDif: Discourse Modality-information Differentiation for Audio-visual Deepfake Detection and Localization
Deepfake technology has rapidly advanced, posing significant threats to information integrity and societal trust. While significant progress has been made in detecting deepfakes, the simultaneous manipulation of audio an…
DeepFake DetectionFace Swappingspeech-recognitionSpeech Recognition+1AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Dataset
The detection and localization of highly realistic deepfake audio-visual content are challenging even for the most advanced state-of-the-art methods. While most of the research efforts in this domain are focused on detec…
DeepFake DetectionFace SwappingTemporal Forgery LocalizationUMMAFormer: A Universal Multimodal-adaptive Transformer Framework for Temporal Forgery Localization
The emergence of artificial intelligence-generated content (AIGC) has raised concerns about the authenticity of multimedia content in various fields. However, existing research for forgery content detection has focused m…
Binary ClassificationTemporal Forgery LocalizationVideo InpaintingGlitch in the Matrix: A Large Scale Benchmark for Content Driven Audio-Visual Forgery Detection and Localization
Most deepfake detection methods focus on detecting spatial and/or spatio-temporal changes in facial attributes and are centered around the binary classification task of detecting whether a video is real or fake. This is …
Binary ClassificationDeepFake DetectionFace SwappingTemporal Forgery LocalizationDo You Really Mean That? Content Driven Audio-Visual Deepfake Dataset and Multimodal Method for Temporal Forgery Localization
Due to its high societal impact, deepfake detection is getting active attention in the computer vision community. Most deepfake detection methods rely on identity, facial attributes, and adversarial perturbation-based sp…
BenchmarkingDeepFake DetectionTemporal Forgery LocalizationForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis
The rapid progress of photorealistic synthesis techniques has reached at a critical point where the boundary between real and manipulated images starts to blur. Thus, benchmarking and advancing digital forgery analysis h…
BenchmarkingClassificationGeneral ClassificationTemporal Forgery LocalizationNot made for each other- Audio-Visual Dissonance-based Deepfake Detection and Localization
We propose detection of deepfake videos based on the dissimilarity between the audio and visual modalities, termed as the Modality Dissonance Score (MDS). We hypothesize that manipulation of either modality will lead to …
Constrained Lip-synchronizationDeepFake DetectionFace SwappingTemporal Forgery Localization