SUDS: Sanitizing Universal and Dependent Steganography
Preston K. Robinette Hanchen D. Wang Nishan Shehadeh Daniel Moyer Taylor T. Johnson
Steganography, or hiding messages in plain sight, is a form of information
hiding that is most commonly used for covert communication. As modern
steganographic mediums include images, text, audio, and video, this
communication method is being increasingly used by bad actors to propagate
malware, exfiltrate data, and discreetly communicate. Current protection
mechanisms rely upon steganalysis, or the detection of steganography, but these
approaches are dependent upon prior knowledge, such as steganographic
signatures from publicly available tools and statistical knowledge about known
hiding methods. These dependencies render steganalysis useless against new or
unique hiding methods, which are becoming increasingly common with the
application of deep learning models. To mitigate the shortcomings of
steganalysis, this work focuses on a deep learning sanitization technique
called SUDS that is not reliant upon knowledge of steganographic hiding
techniques and is able to sanitize universal and dependent steganography. SUDS
is tested using least significant bit method (LSB), dependent deep hiding
(DDH), and universal deep hiding (UDH). We demonstrate the capabilities and
limitations of SUDS by answering five research questions, including baseline
comparisons and an ablation study. Additionally, we apply SUDS to a real-world
scenario, where it is able to increase the resistance of a poisoned classifier
against attacks by 1375%.