That file you downloaded may contain hidden messages that aren’t part of its regular contents. The same technology employed for digital watermarking is also misused by crime rings. Law enforcement must now use steganalysis to detect these messages as part of their investigations. Machine learning is an important tool in the discovery of this secret data.
![](https://storage.googleapis.com/kaggle-media/competitions/UTT/Kaggle_ALASKA.png)
Current methods produce unreliable results, raising false alarms. One reason for inaccuracy is the many different devices and processing combinations. Yet, detection models are trained on a homogeneous dataset. To increase accuracy, researchers must put data hidden within digital images “into the wild” (hence the name ALASKA) to mimic real world conditions.
In the competition, you’ll create an efficient and reliable method to detect secret data hidden within innocuous-seeming digital images. Rather than limiting the data source, these images have been acquired with as many as 50 different cameras (from smartphone to full-format high end) and processed in different fashions. Successful entries will include robust detection algorithms with minimal false positives.
The IEEE WIFS (Workshop on Information Forensics and Security) is eager to make this happen again, as a follow up to the
[ALASKA#1 Challenge](https://alaska.utt.fr/#HallOfFame)
. WIFS is an annual event where researchers gather to discuss emerging challenges, exchange fresh ideas, and share state-of-the-art results and technical expertise in the areas of information security and forensics. WIFS has teamed up with Troyes University of Technology, CRIStAL Lab, Lille University, and CNRS to enable more accurate steganalysis.
Law enforcement officers need better methods to combat criminals using hidden messages. The data science community and other researchers can help with better automated detection. More accurate methods could help catch criminals whose communications are hidden in plain sight.
The challenge is organized by Rémi COGRANNE (UTT), Patrick BAS (CRIStAL / CNRS) and Quentin Giboulot (UTT) ; in addition to Kaggle, we have been greatly helped by the following sponsors:
![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1951250%2F3a24a302d6a43d42087769d57048b566%2Flogo_UTT_CRIStAL.png?generation=1588208625932740&alt=media)
![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1951250%2Fa5c6f8c179e51a4048b581191166425d%2FLogoCNRS_SPS.png?generation=1588208648755636&alt=media)