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Deepfake Video Detection Using Convolutional Vision Transformer

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arxiv 2102.11126 v3 pith:IJ7QQ4IP submitted 2021-02-22 cs.CV

Deressa Wodajo , Solomon Atnafu This is my paper · ORCID
classification cs.CV
keywords convolutionaltransformervisiondetectionfeaturesvideoachievedalter
verification ladder T0 review T1 audit T2 compute T3 formal

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The rapid advancement of deep learning models that can generate and synthesis hyper-realistic videos known as Deepfakes and their ease of access to the general public have raised concern from all concerned bodies to their possible malicious intent use. Deep learning techniques can now generate faces, swap faces between two subjects in a video, alter facial expressions, change gender, and alter facial features, to list a few. These powerful video manipulation methods have potential use in many fields. However, they also pose a looming threat to everyone if used for harmful purposes such as identity theft, phishing, and scam. In this work, we propose a Convolutional Vision Transformer for the detection of Deepfakes. The Convolutional Vision Transformer has two components: Convolutional Neural Network (CNN) and Vision Transformer (ViT). The CNN extracts learnable features while the ViT takes in the learned features as input and categorizes them using an attention mechanism. We trained our model on the DeepFake Detection Challenge Dataset (DFDC) and have achieved 91.5 percent accuracy, an AUC value of 0.91, and a loss value of 0.32. Our contribution is that we have added a CNN module to the ViT architecture and have achieved a competitive result on the DFDC dataset.

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Forward citations

Cited by 13 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Rethinking the Readout: Unlocking Video Backbones for AI-Generated Video Detection

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Replacing the global-pooling readout of a frozen video backbone with a velocity-gated, per-channel-magnitude readout improves AI-generated video detection cross-generator accuracy by several AUC points.

  2. Towards a Universal Synthetic Video Detector: From Face or Background Manipulations to Fully AI-Generated Content

    cs.CV 2024-12 conditional novelty 6.0 of 10

    A single detector trained on face-forgery and game-rendered video beats face-only detectors on background-edited and text-to-video synthetic content.

  3. FairForensics: Seeing Expressions and Parsing Demographics via Vision-Language Modeling for Generalizable Fair Deepfake Detection

    cs.CV 2026-08 reject novelty 5.0 of 10

    FairForensics combines expression-aware visual features, demographic-aware language alignment, and prototype-based fairness losses to improve cross-dataset deepfake detection and demographic fairness.

  4. CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation

    cs.CV 2025-05 conditional novelty 5.0 of 10

    CAD combines cross-modal lip-speech alignment with per-modality artifact distillation and reports 99.96% AUC on IDForge-v2, with strong cross-dataset results.

  5. Cross-Branch Orthogonality for Improved Generalization in Face Deepfake Detection

    cs.CV 2025-05 conditional novelty 5.0 of 10

    A multi-branch face deepfake detector with branch-level and cross-branch orthogonality losses reports higher cross-dataset AUC on Celeb-DF and DFDC when trained on FaceForensics++.

  6. Distilled Transformers with Locally Enhanced Global Representations for Face Forgery Detection

    cs.CV 2024-12 conditional novelty 5.0 of 10

    A distilled transformer with mixture-of-experts, local enhancement, and attention scaling is reported to improve face forgery detection on five deepfake benchmarks.

  7. FRIDAY: Mitigating Unintentional Facial Identity in Deepfake Detectors Guided by Facial Recognizers

    cs.CV 2024-12 conditional novelty 5.0 of 10

    FRIDAY trains a deepfake detector with an extra loss that orthogonalizes its features against a frozen face recognizer, reducing reliance on facial identity and improving cross-domain accuracy.

  8. From Audio Deepfake Detection to AI-Generated Music Detection -- A Pathway and Overview

    cs.SD 2024-11 conditional novelty 5.0 of 10

    A review of AI-generated music detection that proposes intrinsic music features and multimodal fusion as the basis for adapting audio deepfake detection methods.

  9. Revisiting Simple Baselines for In-The-Wild Deepfake Detection

    cs.CV 2025-09 conditional novelty 4.0 of 10

    Finetuned CLIP-pretrained ConvNeXt-base and ViT-b32 classifiers reach 81% accuracy on Deepfake-Eval-2024, within noise of the leading commercial detector's 82%.

  10. SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection

    cs.CV 2025-06 conditional novelty 4.0 of 10

    A spatial-frequency feature fusion network with attention achieves improved accuracy on remote sensing image forgery detection and introduces a stable-diffusion-based benchmark.

  11. Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection

    cs.CV 2025-06 conditional novelty 4.0 of 10

    The paper introduces a deepfake OOD detector that combines reconstruction residual, latent encoding, and softmax confidence, and shows strong results only when real OOD samples are available for training.

  12. Enhancing Deepfake Detection using SE Block Attention with CNN

    cs.CV 2025-06 conditional novelty 3.0 of 10

    Adding SE attention blocks to a small CNN raises deepfake detection accuracy from 91.13% to 94.14% on the StyleGAN subset of DFFD, but the result rests on a single run with questionable comparisons.

  13. Face Deepfakes -- A Comprehensive Review

    cs.CV 2025-02 conditional novelty 3.0 of 10

    A review of face deepfake generation and detection finds that off-the-shelf deepfake tools such as Wav2Lip and SimSwap achieve high attack success rates against lightweight face recognition models.

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