Presentation + Paper
12 June 2023 StegAI: detecting steganography with deep learning
Emily Beatty, Skyler Carlson
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Adversaries or internal threats wishing to exfiltrate sensitive data may choose a method that obfuscates the fact that a transfer is even taking place by camouflaging it inside of allowable data. This technique is known as steganography, which is the practice of concealing data (such as hidden communications or sensitive data of any kind) in an existing data transfer medium, such as images or video. Video streams may be targeted more readily than other media because of the bandwidth they offer compared to single images. Current methods for mitigating the risk associated with data exfiltration through steganography sanitization through unconditional alteration video frames. However, these approaches are limited as they do not detect if a steganography attack is occurring. Deep Neural Networks (DNNs) excel at pattern recognition, presenting an opportunity to leverage them for detection of steganography. Having the capability to detect steganography could prove extremely useful, with uses such as prompting security administrators to investigate further or to immediately halt a video stream. We investigate Least Significant Bit Steganography with both encrypted and cleartext payloads and show that machine learning can detect both.
Conference Presentation
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Emily Beatty and Skyler Carlson "StegAI: detecting steganography with deep learning", Proc. SPIE 12544, Open Architecture/Open Business Model Net-Centric Systems and Defense Transformation 2023, 125440I (12 June 2023);
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Machine learning

Deep learning

Neural networks

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