Introduces TASLE benchmark and MSLoc baseline for temporal localization and explanation of manipulated segments in long videos.
hub Baseline reference
Demamba: Ai-generated video detection on million-scale genvideo benchmark
Baseline reference. 67% of citing Pith papers use this work as a benchmark or comparison.
19
Pith papers citing it
Baseline
67% of classified citations
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citation-role summary
baseline 3
background 2
dataset 1
citation-polarity summary
representative citing papers
CMTA detects AI-generated videos by capturing unnatural temporal stability in visual-textual semantic alignment via joint embeddings and multi-grained temporal modeling, outperforming prior methods in cross-generator tests.
VideoASMR-Bench shows state-of-the-art VLMs fail to reliably detect AI-generated ASMR videos from real ones, though humans can still identify the fakes relatively easily.
MVAD is the first comprehensive benchmark dataset for AI-generated multimodal video-audio detection, with three realistic forgery patterns, high-quality outputs from state-of-the-art models, and diversity across visual styles and content categories.
Motion-based AI video detectors exploit motion biases in evaluation datasets and drop to near-random performance on rebalanced data, while frequency-based detectors remain robust.
Introduces a commercial-model contrastive AIGC video dataset and a hybrid contrastive-MLLM detection framework claiming SOTA performance on realistic video forgery detection.
SpecSem-Net integrates Fourier-based spectral filtering with semantic-guided gated merging to detect AI-generated videos, reporting 87.25% accuracy on a new benchmark of five commercial generators and 95.59% on public datasets.
CAM-VFD detects video forgeries by using cross-attention to identify contradictions between CLIP appearance, VideoMAE motion, and MiDaS depth features.
An SNN-based detector combining multi-channel pseudo-event residuals with frozen semantic features reaches 93.14% mean accuracy on unseen generators under the Pika-trained GenVideo protocol.
ATSS detects AI-generated videos by measuring unnatural repetitive temporal correlations in triple similarity matrices derived from frame visuals and semantic descriptions.
Skyra is an MLLM that detects AI-generated videos by identifying and reasoning over grounded visual artifacts, supported by a new annotated dataset and benchmark.
SAGA is a multi-granular source attribution system for generative AI videos that identifies the exact generator with state-of-the-art accuracy using only 0.5% labeled data per class.
ReConFuse detects AI-generated videos by fusing WF-VAE reconstruction error patterns with multi-frame semantic features via a Mamba-based temporal model.
VINA trains a single detector on images plus video frames using a cross-modal supervised contrastive objective, yielding bidirectional gains and SOTA results on 14 image, video, and in-the-wild benchmarks.
MDMF detects AI-generated images by learning patch-level forensic signatures and quantifying their distributional discrepancies with MMD, yielding larger separation than global methods when micro-defects are present.
Omni-Fake delivers a unified multimodal deepfake benchmark dataset and RL-driven detector that reports gains in accuracy, cross-modal generalization, and explainability over prior baselines.
The SAFE challenge shows measurable progress in detecting synthetic videos across different generators but persistent weaknesses against post-processing operations.
The paper analyzes evolving security and safety threats in generative AI from content generation to agentic actions, noting that attack surfaces expand faster than defenses and that many safeguards require institutional coordination not yet in place.
citing papers explorer
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Explainable Forensics of Manipulated Segments in Untrimmed Long Videos
Introduces TASLE benchmark and MSLoc baseline for temporal localization and explanation of manipulated segments in long videos.
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CMTA: Leveraging Cross-Modal Temporal Artifacts for Generalizable AI-Generated Video Detection
CMTA detects AI-generated videos by capturing unnatural temporal stability in visual-textual semantic alignment via joint embeddings and multi-grained temporal modeling, outperforming prior methods in cross-generator tests.
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VideoASMR-Bench: Can AI-Generated ASMR Videos Fool VLMs and Humans?
VideoASMR-Bench shows state-of-the-art VLMs fail to reliably detect AI-generated ASMR videos from real ones, though humans can still identify the fakes relatively easily.
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MVAD: A Benchmark Dataset for Multimodal AI-Generated Video-Audio Detection
MVAD is the first comprehensive benchmark dataset for AI-generated multimodal video-audio detection, with three realistic forgery patterns, high-quality outputs from state-of-the-art models, and diversity across visual styles and content categories.
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Dataset Biases and Shortcut Learning in Motion-Based AI-Generated Video Detection
Motion-based AI video detectors exploit motion biases in evaluation datasets and drop to near-random performance on rebalanced data, while frequency-based detectors remain robust.
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CoCoVideo: The High-Quality Commercial-Model-Based Contrastive Benchmark for AI-Generated Video Detection
Introduces a commercial-model contrastive AIGC video dataset and a hybrid contrastive-MLLM detection framework claiming SOTA performance on realistic video forgery detection.
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SpecSem-Net: Integrating Spectral and Semantic Features for Robust AI-generated Video Detection
SpecSem-Net integrates Fourier-based spectral filtering with semantic-guided gated merging to detect AI-generated videos, reporting 87.25% accuracy on a new benchmark of five commercial generators and 95.59% on public datasets.
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CAM-VFD: Cross-Attention Multimodal Video Forgery Detection
CAM-VFD detects video forgeries by using cross-attention to identify contradictions between CLIP appearance, VideoMAE motion, and MiDaS depth features.
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Detecting AI-Generated Videos with Spiking Neural Networks
An SNN-based detector combining multi-channel pseudo-event residuals with frozen semantic features reaches 93.14% mean accuracy on unseen generators under the Pika-trained GenVideo protocol.
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ATSS: Detecting AI-Generated Videos via Anomalous Temporal Self-Similarity
ATSS detects AI-generated videos by measuring unnatural repetitive temporal correlations in triple similarity matrices derived from frame visuals and semantic descriptions.
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Skyra: AI-Generated Video Detection via Grounded Artifact Reasoning
Skyra is an MLLM that detects AI-generated videos by identifying and reasoning over grounded visual artifacts, supported by a new annotated dataset and benchmark.
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SAGA: Source Attribution of Generative AI Videos
SAGA is a multi-granular source attribution system for generative AI videos that identifies the exact generator with state-of-the-art accuracy using only 0.5% labeled data per class.
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ReConFuse: Reconstruction-Error Guided Semantic Fusion for AI-Generated Video Detection
ReConFuse detects AI-generated videos by fusing WF-VAE reconstruction error patterns with multi-frame semantic features via a Mamba-based temporal model.
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Video as Natural Augmentation: Towards Unified AI-Generated Image and Video Detection
VINA trains a single detector on images plus video frames using a cross-modal supervised contrastive objective, yielding bidirectional gains and SOTA results on 14 image, video, and in-the-wild benchmarks.
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Micro-Defects Expose Macro-Fakes: Detecting AI-Generated Images via Local Distributional Shifts
MDMF detects AI-generated images by learning patch-level forensic signatures and quantifying their distributional discrepancies with MMD, yielding larger separation than global methods when micro-defects are present.
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Omni-Fake: Benchmarking Unified Multimodal Social Media Deepfake Detection
Omni-Fake delivers a unified multimodal deepfake benchmark dataset and RL-driven detector that reports gains in accuracy, cross-modal generalization, and explainability over prior baselines.
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Advancing Reliable Synthetic Video Detection: Insights from the SAFE Challenge
The SAFE challenge shows measurable progress in detecting synthetic videos across different generators but persistent weaknesses against post-processing operations.
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From AI-Generated Content to Agentic Action: Security and Safety Threats in Generative AI
The paper analyzes evolving security and safety threats in generative AI from content generation to agentic actions, noting that attack surfaces expand faster than defenses and that many safeguards require institutional coordination not yet in place.
- DVAR: Adversarial Multi-Agent Debate for Video Authenticity Detection