2026· Poster Volume 0007 The 2026 Twenty-Second International Conference on Intelligent Computing July 23-26, 2026 Toronto, Canada· 0 citations
TL;DR
This work proposes a parameter-efficient, video-based deepfake detection framework that leverages a frozen foundation model encoder coupled with a lightweight spatio-temporal decoder, complemented by a Bidirectional Spatio-Temporal decoder that models local temporal transitions and bidirectional temporal dependencies across sampled frames, enabling robust temporal reasoning within each video clip.
Abstract
The advancement of deep generative models facilitates realistic synthetic facial videos, threatening social trust and digital security. Existing detection methods achieve strong in-domain performance but suffer from cross-dataset degradation, primarily due to overfitting to dataset-specific spatial artifacts. To address these challenges, we propose a parameter-efficient, video-based deepfake detection framework that leverages a frozen foundation model encoder coupled with a lightweight spatio-temporal decoder. First, we introduce a Component-Aware Spatial Enhancement (CASE) module that selectively accentuates manipulation-prone facial regions, such as the eyes, mouth and nose, while capturing global-local structural inconsistencies, thereby enabling the detection of subtle artifacts. Second, it is complemented by a Bidirectional Spatio-Temporal (Bi-ST) decoder that models local temporal transitions and bidirectional temporal dependencies across sampled frames, enabling robust temporal reasoning within each video clip. Without fine-tuning the backbone network, our framework achieves robust cross-dataset generalization by jointly reasoning about spatial and temporal anomalies. Finally, extensive experiments demonstrate that the proposed method performs competitively against strong baselines, particularly under cross-dataset evaluation.
This work proposes a spatial-temporal model with two key components: one targeting artifacts within individual frames and the other analyzing inconsistencies across consecutive frames, both leveraging a bidirectional long short-term memory (Bi-LSTM) mechanism.
Ahmed Tammam, Hatem Abdel-Kader, Amira Abdelatey et al.· Signal, Image and Video Proc...· 0 citations
Deepfakes pose a growing threat to digital media integrity, as many detection methods rely mainly on spatial artifacts and insufficiently model the temporal attention dynamics that distinguish authentic from manipulated videos. This paper proposes Adaptive Selective Temporal Attention Regularization (ASTAR) Loss, a feature-guided temporal regularization loss for robust and explainable video-based deepfake detection. ASTAR stabilizes temporal attention by penalizing unnecessary attention shifts between visually similar consecutive frames while relaxing the penalty during legitimate temporal variations caused by head motion, facial expression changes, blinking, or illumination fluctuation. The proposed framework integrates ResNeXt50 for spatial feature extraction, Bidirectional Long Short-Term Memory (BiLSTM) for temporal modeling, and Multi-Head Attention for selective temporal feature aggregation, with ASTAR applied as an auxiliary training objective alongside the primary Cross-Entropy classification loss. Unlike uniform attention-smoothing approaches, ASTAR adaptively balances temporal coherence and motion flexibility, improving attention stability without suppressing natural video dynamics. Experiments on FaceForensics++, DFDC, and Celeb-DF demonstrate strong intra-dataset performance, achieving 98.49% accuracy and 100.00% AUC on FaceForensics++, 98.18% accuracy and 99.92% AUC on DFDC, and 98.29% accuracy and 99.97% AUC on Celeb-DF. In single-source cross-dataset evaluation, where the model was trained on FaceForensics + + and evaluated on unseen datasets without fine-tuning, ASTAR achieved 93.83% AUC on DFDC and 94.60% AUC on Celeb-DF. These results indicate that ASTAR improves classification performance, temporal attention coherence, and interpretability under the evaluated experimental settings, while further validation using multi-source training and additional unseen datasets remains necessary for broader generalization claims.
Ayat Abd-Muti Alrawahneh, Siti Norul Huda Sheikh Abdullah, Amelia Natasya Abdul Wahab et al.· Discover Artificial Intellig...· 0 citations
A diffusion-based framework with a stage-wise multi-condition guidance mechanism that enhances both structural and textural fidelity and compares with recent image-to-image translation and diffusion-based baselines to observe competitive performance in both visual coherence and identity preservation.
Yue Que, Xuegui Cheng, Shuqian Shi et al.· Neural Networks· 0 citations
The continuous emergence of high-quality video deepfakes requires detectors that continually adapt to new forgery patterns, yet existing approaches, which are designed for deepfake images, fail to capture video-specific cues. Unlike deepfake images that contain only spatial artifacts, deepfake videos leave distinct evidence along both spatial and temporal axes, necessitating the separate preservation of each modality during sequential model updates. To overcome this limitation, we introduce a continual deepfake video detection framework, Modality-Specific Frequency Distillation (MSFD), that explicitly decomposes video features into spatial, temporal, and spatiotemporal modalities in the frequency domain. This decomposition enables independent preservation of each modality, as different deepfake video types exhibit varying reliance on spatial and temporal cues across tasks. Furthermore, MSFD adopts a cross-modality decorrelation loss that encourages spatiotemporal representations to remain orthogonal to single-modality cues. Extensive experiments show that our framework achieves stronger adaptation and preserves performance more effectively than state-of-the-art methods across diverse continual deepfake video scenarios.
Taehoon Kim, Jongwook Choi, Heejae Jo et al.· 0 citations
Detecting audio-visual DeepFake (AVDeepFake) is becoming increasingly important as synthetic media tools become widely accessible and spread across consumer devices. In this study, we present an Detecting audio-visual DeepFakes (AV-DeepFakes) has become increasingly critical with the rapid proliferation of accessible synthetic media generation tools across consumer platforms. In this work, we propose a highperformance, deployment-efficient AV-DeepFake detection framework tailored for real-world consumer devices. The proposed model integrates a 3D convolutional visual encoder with a 2D convolutional audio encoder to learn synchronized multimodal representations, effectively capturing spatial, spectral, and prosodic inconsistencies inherent in manipulated content. To detect temporal forgeries, we introduce a bidirectional complementary boundary module that precisely localizes manipulation onsets and offsets. A cross-modal attention fusion mechanism aggregates modality-specific cues, while an uncertainty-aware gating strategy suppresses unreliable signals to improve robustness. Furthermore, a cross-modal discrepancy minimization loss encourages alignment for genuine samples while maximizing divergence for forged content, strengthening multimodal consistency learning. Extensive evaluations on FaceForensics++ and LAV-DF demonstrate the effectiveness of the proposed approach, achieving 97.1% AUC for clip-level detection and an 81.2% F1-score for temporal boundary localization, while reducing inference time by 5× compared with transformer-based methods.
Nasir Saleem, Adeel Hussain, Sami Bourouis et al.· International Journal of Int...· 0 citations
Micro-temporal facial expressions consist of brief and subtle muscle activations that convey genuine emotional states. Accurately modeling these transient dynamics is challenging due to their low intensity, short duration, and limited spatial variation, which often hinder the performance of conventional convolutional and recurrent architectures. To address these limitations, this paper introduces a Spatio-Temporal Self-Attention Network (STTN) that leverages transformer-based self-attention to effectively capture finegrained dependencies across both spatial regions and short temporal intervals. The proposed framework focuses on learning discriminative representations of micro-temporal facial movements by emphasizing relevant facial regions and their temporal evolution. Extensive experiments are conducted on high-frame-rate micro-expression benchmarks, including SAMM, CASME II, and CAS(ME)2 datasets. The results demonstrate that the proposed model achieves superior performance compared to existing state-of-the-art approaches, highlighting the effectiveness of self-attention mechanisms in modeling subtle and rapid facial dynamics.
J. R, K. Sindhubala, D. Kiruba et al.· International Conference on...· 0 citations
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