Enhancing Steganography Detection with AI: Fine-Tuning a Deep Residual Network for Spread Spectrum Image Steganography
Oleksandr Kuznetsov, Emanuele Frontoni, Kyrylo Chernov, Kateryna Kuznetsova, Ruslan Shevchuk, Mikolaj Karpinski · 2024
Read Enhancing Steganography Detection with AI: Fine-Tuning a Deep Residual Network for Spread Spectrum Image Steganography by Oleksandr Kuznetsov, Emanuele Frontoni, Kyrylo Chernov, Kateryna Kuznetsova, Ruslan Shevchuk, Mikolaj Karpinski (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11644885.txt.
Description
Oleksandr Kuznetsov and five co-authors fine-tune a deep network to catch a hard-to-spot form of image steganography in this 2024 paper. A leading detector, SRNet, handled several hiding methods well but lagged on spread spectrum embedding. The team retrained it on data from that method.
After fine-tuning, detection of spread spectrum images improved significantly. The gain cost a small drop on the other hiding techniques. The authors read the trade-off as a general lesson in adapting forensic models: specialization helps the target but can dull breadth. They suggest multi-task learning and other strategies to keep both. The work shows AI detectors can adapt, if adaptation is balanced.
Catalog record
| CREATOR | Oleksandr Kuznetsov, Emanuele Frontoni, Kyrylo Chernov, Kateryna Kuznetsova, Ruslan Shevchuk, Mikolaj Karpinski |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 58 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |