In recent years, the criminal use of information hiding techniques (i.e. steganography) in digital media such as images, video, audio and text files has increased rapidly. An important reason for this is that many steganographic tools are publicly available. As an example, the platform GitHub2 is currently reporting more than 4,800 steganography related results in its repository. Moreover, this multitude of tools is often made available as program source code packages. Consequently, perpetrators can easily and selectively pick, adapt and combine information hiding tools for their criminal activities. We are observing an increase in the use of steganographic methods in a wide variety of types of crime including child pornography, industrial espionage, criminal attacks on enterprises, credit card fraud, system intrusion, and backdoor injection and delivery methods.
The increasing complexity of security challenges combined with the accumulation of significant amounts of digital data calls for better and more widespread use of artificial intelligence (AI) capabilities for law enforcement agencies, also in relation to information hiding techniques. AI can provide benefits to forensic institutes on a number of levels given the right understanding, tools, data and protection while increasing the validation and making it explainable for court. You will work in the Forensic Image Analysis and Biometrics team of the Digital and Biometric Traces division. The team utilises data analysis to respond to forensic investigation queries about the use of evidence in legal proceedings, for example. You will work closely with the University of Amsterdam’s Informatics Institute (team Multix), where you will obtain your PhD. In addition, you will contribute to EU projects on Stego-analysis and the processing of large amounts of multimedia data.
Applicable research questions