mlsanityData to 7 Oct 2026

Explainable Deepfake Video Detection using Convolutional Neural Network and CapsuleNet

arXiv 2404.12841 ↗First posted 19 Apr 2024cs.CV cs.LG eess.IVcited by 2

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Abstract (arXiv)

Deepfake technology, derived from deep learning, seamlessly inserts individuals into digital media, irrespective of their actual participation. Its foundation lies in machine learning and Artificial Intelligence (AI). Initially, deepfakes served research, industry, and entertainment. While the concept has existed for decades, recent advancements render deepfakes nearly indistinguishable from reality. Accessibility has soared, empowering even novices to create convincing deepfakes. However, this accessibility raises security concerns.The primary deepfake creation algorithm, GAN (Generative Adversarial Network), employs machine learning to craft realistic images or videos. Our objective is to utilize CNN (Convolutional Neural Network) and CapsuleNet with LSTM to differentiate between deepfake-generated frames and originals. Furthermore, we aim to elucidate our model's decision-making process through Explainable AI, fostering transparent human-AI relationships and offering practical examples for real-life scenarios.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 25 of its 49 references that are in the map. First posted on arXiv 19 Apr 2024.

Builds on

25 of 49 references.

References · 25

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13 more
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Citing papers per year: 2025: 2.

2 papers.

  1. 2025ExDDV: A New Dataset for Explainable Deepfake Detection in Videocited by 10
  2. 2025Fair and Interpretable Deepfake Detection in Videoscited by 0