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Deepfake video detection

Source article: Deepfake Detection In Multi-Model Using Deep Learning

Artificial Intelligence (AI) refers to the broad field where machines are trained to carry out tasks such as learning, reasoning and decision-making.Deep Learning (DL) is a branch of AI that relies on multi-layered neural networks to extract features automatically, handling complex data like images and videos with high accuracy, which makes it well-suited for deepfake detection.Current detection systems mostly depend on Convolutional Neural Networks (CNNs) to examine spatial artifacts in video frames, alongside…

By the Truvace desk1 min read1 sourcePeer-reviewedTRV-2026-2063· cite & verify
Deepfake Detection In Multi-Model Using Deep Learning
Deepfake detection with and without content warnings by Lewis, Andrew; Vu, Patrick; Duch, Raymond M.; Chowdhury, Areeq. CC BY 4.0 · https://creativecommons.org/licenses/by/4.0
Trace impact readingContested
P 66The P score combines the specificity and measured human impact of the grounded problem claim with the strength of this Trace’s cited sources.

Both sides are scored from claims and sources, not community votes.

G 65The G score combines the specificity and measured human impact of the grounded gain claim with the strength of this Trace’s cited sources.

In brief

The authors propose a multi-modal deep learning system for spotting deepfakes. It pairs CNN-based video preprocessing with LSTM temporal analysis and audio feature extraction, and it is retrained on an ongoing basis.

Deepfakes are a vector for fraud and deception, so detectors that hold up on poor-quality or fast-changing video would matter. The reported accuracy comes from a single benchmark, and the text does not show how the system performs on real-world content outside it.

Main points

  1. Current detectors mostly rely on CNNs to examine spatial artifacts in video frames.
  2. Frequency-domain analysis is used alongside CNNs to catch subtle inconsistencies.
  3. The proposed framework adds LSTM temporal analysis and audio feature extraction.
  4. The system includes ongoing retraining to improve adaptability.
  5. The reported accuracy is 98.47% on the FaceForensics++ benchmark.

The gain

A framework combining CNN video preprocessing, LSTM temporal analysis, audio features and ongoing retraining reached 98.47% accuracy on the FaceForensics++ benchmark for deepfake detection.

The problem

Existing CNN and frequency-domain detectors fall short on low-quality, compressed or rapidly changing fake videos.

The rundown

The paper describes deep learning as a branch of AI that uses multi-layered neural networks to extract features automatically from complex data such as images and videos, which the authors say suits deepfake detection.

Existing approaches produce solid results on benchmark datasets and underpin much detection research. The authors say these approaches weaken on low-quality, compressed or rapidly changing fake videos.

The new framework combines video preprocessing, temporal analysis of frame sequences and audio features. The authors say these elements improve robustness, adaptability and accuracy in practical settings.

What this doesn’t fix

The only quantitative evidence given is accuracy on one benchmark, FaceForensics++, so the abstract does not show performance on other datasets or real-world conditions.

Sources

  1. Peer-reviewedInternational Journal of Drug Delivery Technology2026-10-19

The debate