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(2019). Deep residual network for steganalysis of digital +images. IEEE Transactions on Information Forensics and Security, 14(5), +1181–1193. +https://doi.org/10.1109/TIFS.2018.2871749 + + + Statistically undetectable JPEG +steganography: Dead ends challenges, and opportunities + Fridrich + Proceedings of the 9th workshop on multimedia +& security + 10.1145/1288869.1288872 + 9781595938572 + 2007 + Fridrich, J., Pevný, T., & +Kodovský, J. (2007). Statistically undetectable JPEG steganography: Dead +ends challenges, and opportunities. Proceedings of the 9th Workshop on +Multimedia & Security, 3--14. +https://doi.org/10.1145/1288869.1288872 + + + Uniform embedding for efficient JPEG +steganography + Guo + IEEE Transactions on Information Forensics +and Security + 5 + 9 + 10.1109/TIFS.2014.2312817 + 2014 + Guo, L., Ni, J., & Shi, Y. Q. +(2014). Uniform embedding for efficient JPEG steganography. IEEE +Transactions on Information Forensics and Security, 9(5), 814–825. +https://doi.org/10.1109/TIFS.2014.2312817 + + + A graph–theoretic approach to +steganography + Hetzl + Communications and multimedia +security + 10.1007/11552055_12 + 978-3-540-31978-8 + 2005 + Hetzl, S., & Mutzel, P. (2005). A +graph–theoretic approach to steganography. In J. Dittmann, S. +Katzenbeisser, & A. Uhl (Eds.), Communications and multimedia +security (pp. 119–128). Springer Berlin Heidelberg. +https://doi.org/10.1007/11552055_12 + + + Universal distortion function for +steganography in an arbitrary domain + Holub + EURASIP Journal on Information +Security + 1 + 2014 + 10.1186/1687-417X-2014-1 + 2014 + Holub, V., Fridrich, J., & +Denemark, T. (2014). Universal distortion function for steganography in +an arbitrary domain. EURASIP Journal on Information Security, 2014(1), +1–13. https://doi.org/10.1186/1687-417X-2014-1 + + + Moving steganography and steganalysis from +the laboratory into the real world + Ker + Proceedings of the first ACM workshop on +information hiding and multimedia security + 10.1145/2482513.2482965 + 2013 + Ker, A. D., Bas, P., Böhme, R., +Cogranne, R., Craver, S., Filler, T., Fridrich, J., & Pevný, T. +(2013). Moving steganography and steganalysis from the laboratory into +the real world. Proceedings of the First ACM Workshop on Information +Hiding and Multimedia Security, 45–58. +https://doi.org/10.1145/2482513.2482965 + + + Detection of classifier inconsistencies in +image steganalysis + Lerch-Hostalot + Proceedings of the ACM workshop on +information hiding and multimedia security + 10.1145/3335203.3335738 + 9781450368216 + 2019 + Lerch-Hostalot, D., & Megı́as, D. +(2019). Detection of classifier inconsistencies in image steganalysis. +Proceedings of the ACM Workshop on Information Hiding and Multimedia +Security, 222--229. +https://doi.org/10.1145/3335203.3335738 + + + Subsequent embedding in targeted image +steganalysis: Theoretical framework and practical +applications + Megı́as + IEEE Transactions on Dependable and Secure +Computing + 2 + 20 + 10.1109/tdsc.2022.3154967 + 2023 + Megı́as, D., & Lerch-Hostalot, D. +(2023). Subsequent embedding in targeted image steganalysis: Theoretical +framework and practical applications. IEEE Transactions on Dependable +and Secure Computing, 20(2), 1403–1421. +https://doi.org/10.1109/tdsc.2022.3154967 + + + A new cost function for spatial image +steganography + Li + 2014 IEEE international conference on image +processing (ICIP) + 10.1109/icip.2014.7025854 + 2014 + Li, B., Wang, M., Huang, J., & +Li, X. (2014). A new cost function for spatial image steganography. 2014 +IEEE International Conference on Image Processing (ICIP), 4206–4210. +https://doi.org/10.1109/icip.2014.7025854 + + + Defending against statistical +steganalysis + Provos + 10th USENIX security symposium (USENIX +security 01) + 2001 + Provos, N. (2001). Defending against +statistical steganalysis. 10th USENIX Security Symposium (USENIX +Security 01), 1–13. +https://www.usenix.org/conference/10th-usenix-security-symposium/defending-against-statistical-steganalysis + + + An implementation of key-based digital signal +steganography + Sharp + Information hiding + 2137 + 10.1007/3-540-45496-9_2 + 2001 + Sharp, T. (2001). An implementation +of key-based digital signal steganography. In Information hiding (Vol. +2137, pp. 13–26). Springer. +https://doi.org/10.1007/3-540-45496-9_2 + + + ImageNet pre-trained CNNs for JPEG +steganalysis + Yousfi + 2020 IEEE international workshop on +information forensics and security (WIFS) + 10.1109/WIFS49906.2020.9360897 + 2020 + Yousfi, Y., Butora, J., Khvedchenya, +E., & Fridrich, J. (2020). ImageNet pre-trained CNNs for JPEG +steganalysis. 2020 IEEE International Workshop on Information Forensics +and Security (WIFS), 1–6. +https://doi.org/10.1109/WIFS49906.2020.9360897 + + + SteganoGAN: High capacity image steganography +with GANs + Zhang + 2019 + Zhang, K. A., Cuesta-Infante, A., Xu, +L., & Veeramachaneni, K. (2019). SteganoGAN: High capacity image +steganography with GANs. +https://arxiv.org/abs/1901.03892 + + + + + + From dfc69154ee500a243db1739a288a98ab4e43dbec Mon Sep 17 00:00:00 2001 From: The Open Journals editorial robot <89919391+editorialbot@users.noreply.github.com> Date: Tue, 16 Jan 2024 14:07:03 +0000 Subject: [PATCH 3/3] Creating 10.21105.joss.05982.jats --- joss.05982/10.21105.joss.05982.jats | 465 ++++++++++++++++++++++++++++ 1 file changed, 465 insertions(+) create mode 100644 joss.05982/10.21105.joss.05982.jats diff --git a/joss.05982/10.21105.joss.05982.jats b/joss.05982/10.21105.joss.05982.jats new file mode 100644 index 0000000000..96fe91c6b7 --- /dev/null +++ b/joss.05982/10.21105.joss.05982.jats @@ -0,0 +1,465 @@ + + +
    + + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +5982 +10.21105/joss.05982 + +Aletheia: an open-source toolbox for +steganalysis + + + +https://orcid.org/0000-0003-2602-672X + +Lerch-Hostalot +Daniel + + + + + + +https://orcid.org/0000-0002-0507-7731 + +Megías +David + + + + + + + +Internet Interdisciplinary Institute (IN3), Barcelona, +Spain + + + + +Universitat Oberta de Catalunya (UOC), Barcelona, +Spain + + + + +CYBERCAT-Center for Cybersecurity Research of Catalonia, +Barcelona, Spain + + + + +12 +1 +2024 + +9 +93 +5982 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +steganalysis +Python +image processing + + + + + + Summary +

    Steganalysis is the practice of detecting the presence of hidden + information within digital media, such as images, audio, or video. It + involves analyzing the media for signs of steganography, which is a + set of techniques used to conceal information within the carrier file. + Steganalysis techniques can include statistical analysis, visual + inspection, and machine learning algorithms to uncover hidden data. + The goal of steganalysis is to determine whether a file contains + covert information and potentially identify the steganographic method + used.

    +

    Steganalysis has become increasingly important in the face of + rising spying and stegomalware threats, particularly in the context of + data exfiltration. In this scenario, malicious actors leverage + steganographic techniques to conceal sensitive data within + innocent-looking files, evading traditional security measures. By + detecting and analyzing such covert communication channels, + steganalysis helps to identify and prevent data exfiltration attempts, + safeguarding critical information and preventing it from falling into + the wrong hands.

    +

    In recent years, there has been a significant growth in the + interest of researchers towards the field of steganalysis. The + application of deep learning + (Boroumand + et al., 2019; + Yousfi + et al., 2020) in steganalysis has opened up new avenues for + research, leading to improved detection rates and enhanced accuracy. + As the field continues to evolve, experts are actively exploring novel + architectures and training methodologies to further refine the + performance of deep learning-based steganalysis.

    +
    + + Statement of need +

    Aletheia addresses two main needs. Firstly, it aims to provide + specialized analysts with a tool that implements modern steganalysis + algorithms, leveraging deep learning techniques. These algorithms are + designed to effectively handle even the most advanced steganography + techniques. Secondly, Aletheia serves as a valuable tool for + researchers by simplifying the process of conducting experiments and + comparing methods. It includes simulators for common algorithms + (Hetzl + & Mutzel, 2005; + Provos, + 2001; + Sharp, + 2001) as well as state-of-the-art steganography methods + (Fridrich + et al., 2007; + Guo + et al., 2014; + Holub + et al., 2014; + Li + et al., 2014; + Zhang + et al., 2019), enabling researchers to prepare and evaluate + their work efficiently.

    +

    On the other hand, to the best of the authors’ knowledge, Aletheia + stands out as the sole steganalysis tool currently available that + incorporates the latest detection techniques + (Lerch-Hostalot + & Megı́as, 2019; + Megı́as + & Lerch-Hostalot, 2023) specifically designed to address + the challenges posed by Cover Source Mismatch (CSM) in real-world + steganalysis scenarios + (Ker + et al., 2013). This capability is particularly significant for + conducting effective steganalysis in practical applications.

    +
    + + Description +

    Aletheia incorporates various image steganography simulators, as + well as tools for preparing datasets with different payload sizes + using these simulators. This enables researchers to prepare + experiments for their articles. Therefore, having access to the + original implementations of the different simulators is relevant. + Since it is common for these implementations to be developed in + Matlab, Aletheia includes several of these simulators in its original + code, slightly modified to be executed using Octave. These simulators + frequently have licenses that can be incompatible with the MIT license + used by Aletheia. For this reason, this code is in an external + repository and is downloaded separately after a confirmation by the + user. Aletheia also implements other simulators directly in Python, + the programming language of Aletheia, as well as tools that directly + utilize their binaries.

    +

    These simulators can be used to conduct experiments, as shown in + the following example. Here, you can observe how the simulator uses + the HILL algorithm and embeds a random payload, which ranges from 5% + to 25% of the image’s maximum capacity when hiding 1 bit per pixel, + within images sourced from the “images” folder. The resulting data is + then saved in the “experiment” folder.

    + ./aletheia.py hill-color-sim images 0.05-0.25 experiment +

    Although Aletheia allows for the preparation of experiments using + multiple simulators, its primary objective is steganalysis. This is + achieved through the implementation of various structural attacks on + LSB replacement, as well as employing deep learning techniques with + models optimized for a vast range of steganography algorithms. These + algorithms include both commonly used tools in the real world and + state-of-the-art steganographic methods.

    +

    Aletheia also offers automated tools that allow for a preliminary + analysis, greatly aiding the investigation of the steganalyst. For + example, the automated analysis below showcases the modeled + probabilities of each image being generated using various + steganographic methods.

    + $ ./aletheia.py auto actors/A2/ + + Outguess Steghide nsF5 J-UNIWARD * +----------------------------------------------------------- +2.jpg [1.0] [1.0] [0.9] 0.3 +4.jpg [1.0] [1.0] [0.7] 0.3 +10.jpg 0.0 [1.0] 0.3 0.2 +6.jpg 0.0 [1.0] 0.1 0.0 +7.jpg [1.0] [1.0] 0.3 0.1 +8.jpg 0.0 [1.0] 0.1 0.2 +9.jpg [0.8] [1.0] [0.7] 0.1 +1.jpg [1.0] [1.0] [0.8] 0.1 +3.jpg [1.0] [1.0] [1.0] 0.3 +5.jpg 0.0 0.1 [0.7] [0.6] + +* Probability of steganographic content using the indicated method. +

    Aletheia offers many other functionalities for steganalysis that + are not covered in this article and can be found in Aletheia’s + documentation + (github.com/daniellerch/aletheia). + Some examples include calibration attacks, custom model preparation, + high-pass filters, image difference analysis using pixels and DCT + coefficients, DCI techniques to deal with Cover Source Mismatch (CSM), + and more.

    +
    + + Acknowledgements +

    We acknowledge the funding obtained by the Detection of fake newS + on SocIal MedIa pLAtfoRms (DISSIMILAR) project from the EIG + CONCERT-Japan with grant PCI2020-120689-2 (Government of Spain), and + the PID2021-125962OB-C31 “SECURING” project granted by the Spanish + Ministry of Science and Innovation. We wish to express our sincere + gratitude towards NVIDIA Corporation for their generous donation of an + NVIDIA TITAN Xp GPU card, which has been instrumental in the training + of our models.

    +
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