REVEAL: A large-scale comprehensive image dataset for steganalysis

Detection methodologies for steganography are a topic of study both within academia and in law enforcement. For the development of detection methods and the validation of their use for law enforcement, a large-scale representative dataset is essential. Current datasets are lacking in terms of representing real-life steganography, as they only include low resolution images, are taken with only a few different cameras, and are validated with only a minimal number of steganography methods. A new large-scale comprehensive image steganography dataset is needed with many typical examples of steganography one could encounter in casework. To that end, we present the REVEAL dataset containing 100.006 images taken with more than 50 different cameras. The set contains a rich variety of images, the attributes of which have a wide distribution. There are for example over 200 different sizes, ranging from 256x256 to 7680x4320. All 100.006 images have then been subjected to many different chains of image preprocessing steps. After the preprocessing, a total of more than 50 different image steganography algorithms were used to hide information in the images. This results in three image sets namely: original, preprocessed, and stego, in total more than 300.000 images. This properly annotated dataset can help to achieve accurate detection using supervised machine-learning based methods. At the same time, this dataset can be used for both forensic evaluation and validation, thus improving the applicability of detection methods. The dataset with full annotations, algorithms, and results is made publicly available.