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Spatiality-Frequency Domain Video Forgery Detection System Based on ResNet-LSTM-CBAM and DCT Hybrid Network — arXiv:2609.27904

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.27904
License (as archived): CC0-1.0
Archived: 2026-10-08T12:35:07.681Z
SHA-256 of the archived text: 71523ec92f97c06636b03d3bddc5bfc7a9d5bfc3018e8198ce9b39218d116060
Stable URL of this page: https://metraipolis.com/documento/dc_eeb38cfdbcac

How to cite

Spatiality-Frequency Domain Video Forgery Detection System Based on ResNet-LSTM-CBAM and DCT Hybrid Network — arXiv:2609.27904 — arXiv (en), https://arxiv.org/abs/2609.27904 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_eeb38cfdbcac (Metraipolis archive dc_eeb38cfdbcac, sha256 71523ec92f97c06636b03d3bddc5bfc7a9d5bfc3018e8198ce9b39218d116060)

BibTeX

@misc{dceeb38cfdbcac,
  title = {Spatiality-Frequency Domain Video Forgery Detection System Based on ResNet-LSTM-CBAM and DCT Hybrid Network — arXiv:2609.27904},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.27904},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_eeb38cfdbcac; sha256 71523ec92f97c06636b03d3bddc5bfc7a9d5bfc3018e8198ce9b39218d116060},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.27904. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

As information technology advances, digital content has become widely adopted across diverse fields such as news broadcasting, entertainment, commerce, and forensic investiga?tion. However, the availability of sophisticated multimedia editing tools has significantly increased the risk of video and image forgery, raising serious concerns about content authenticity at both societal and individual this http URL address the growing need for robust and accurate detection methods, this study proposes a novel video forgery detection model that integrates both spatial and frequency-domain features. The model is built on a ResNet-LSTM framework enhanced by a Convolutional Block Attention Module (CBAM) for spatial feature extraction, and further incorporates Discrete Cosine Transform (DCT) to capture frequency domain information. Comprehensive experiments were conducted on several mainstream benchmark datasets, encompassing a wide range of forgery scenarios. The results demonstrate that the proposed model achieves superior performance in distinguishing between authentic and manipulated videos. Additional ablation and comparative studies confirm the contribution of each component in the architecture, offering deeper insight into the models capacity. Overall, the findings support the proposed approach as a promising solution for enhancing the reliability of video authenticity analysis under complex conditions.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-24 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.27904 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-08T12:12:35.748Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-24?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).