Detection of Malicious Images in Production-Quality Scenarios with the SIMARGL Toolkit
Author:
Affiliation:
1. CNR, Italy
2. Netzfactor GmbH, Germany
3. Orange Polska, Poland
4. FernUniversität in Hagen, Germany
5. Warsaw University of Technology, Poland
Funder
European Commission - H2020
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3538969.3544469
Reference22 articles.
1. Benedikt Boehm. 2014. Stegexpose-A tool for detecting LSB steganography. arXiv preprint arXiv:1410.6656(2014). Benedikt Boehm. 2014. Stegexpose-A tool for detecting LSB steganography. arXiv preprint arXiv:1410.6656(2014).
2. Trends and Challenges in Network Covert Channels Countermeasures
3. Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection
4. Kernel-level tracing for detecting stegomalware and covert channels in Linux environments
5. MalJPEG: Machine Learning Based Solution for the Detection of Malicious JPEG Images
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