Copy-Move Forgery Detection and Localization via Partial Audio Matching

Maksimovic M, Cuccovillo L, Aichroth P. Copy-Move Forgery Detection and Localization via Partial Audio Matching. In: AES Conference: 2019 AES International Conference on Audio Forensics. Audio Engineering Society; 2019. Paper 24. Available from: https://aes.org/publications/elibrary-page/?id=20474

@inproceedings{Maksimovic2019_20474,
  author = {Maksimovic, Milica and Cuccovillo, Luca and Aichroth, Patrick},
  title = {{Copy-Move Forgery Detection and Localization via Partial Audio Matching}},
  booktitle = {AES Conference: 2019 AES International Conference on Audio Forensics},
  note = {Paper 24},
  year = {2019},
  month = jun,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=20474}
}

TY  - CPAPER
TI  - Copy-Move Forgery Detection and Localization via Partial Audio Matching
AU  - Maksimovic, Milica
AU  - Cuccovillo, Luca
AU  - Aichroth, Patrick
T2  - AES Conference: 2019 AES International Conference on Audio Forensics
M1  - Paper 24
PY  - 2019
DA  - 2019/06/06
UR  - https://aes.org/publications/elibrary-page/?id=20474
PB  - Audio Engineering Society
LA  - en
AB  - In this paper, we present a new approach for detecting and localizing copy-move forgeries within digital audio recordings. The approach is based on an audio ?ngerprinting and matching algorithm that was originally designed for partial audio matching without a query for large datasets, which was now successfully adapted to the speci?c requirements of copy-move forgery detection, i.e. short segment duration and high reliability. Thanks to the characteristics of the original algorithm, our proposed approach does not require pre-segmentation, and shows high accuracy for detection and localization of copy move forgery, including mismatching background noise, thereby signi?cantly extending the state-of-the art.
ER  -