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In this paper, we present a new reference dataset simulating digital evidence for image steganography. Steganography detection is a digital image forensic topic that is relatively unknown in practical forensics, although stego app use in the wild is on the rise. This paper introduces the first database consisting of mobile phone photographs and stego images produced from mobile stego apps, including a rich set of side information, offering simulated digital evidence. StegoAppDB, a steganography apps forensics image database, contains over 810,000 innocent and stego images using a minimum of 10 different phone models from 24 distinct devices, with detailed provenanced data comprising a wide range of ISO and exposure settings, EXIF data, message information, embedding rates, etc. We develop a camera app, Cameraw, specifically for data acquisition, with multiple images per scene, saving simultaneously in both DNG and high-quality JPEG formats. Stego images are created from these original images using selected mobile stego apps through a careful process of reverse engineering. StegoAppDB contains cover-stego image pairs including for apps that resize the stego dimensions. We retain the original devices and continue to enlarge the database, and encourage the image forensics community to use StegoAp-pDB. While designed for steganography, we discuss uses of this publicly available database to other digital image forensic topics.

Fast track article for IS&T International Symposium on Electronic Imaging 2020: Media Watermarking, Security, and Forensics proceedings.

The processing power of smartphones supports steganographic algorithms that were considered to be too computationally intensive for handheld devices. Several steganography apps are now available on mobile phones to support covert communications using digital photographs. This chapter focuses on two key questions: How effectively can a steganography app be reverse engineered? How can this knowledge help improve the detection of steganographic images and other related files? Two Android steganography apps, PixelKnot and Da Vinci Secret Image, are analyzed. Experiments demonstrate that they are constructed in very different ways and provide different levels of security for hiding messages. The results of detecting steganography files, including images generated by the apps, using three software packages are presented. The results point to an urgent need for further research on reverse engineering steganography apps and detecting images produced by these apps.

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