Sign In

Communications of the ACM

ACM Careers

Hidden Messages in Digital Images Are Researcher's Passion


View as: Print Mobile App Share: Send by email Share on reddit Share on StumbleUpon Share on Hacker News Share on Tweeter Share on Facebook
Binghamton University Distinguished Professor Jessica Fridrich

"It can be a little frustrating because the field as well as the technology are developing so fast," says Binghamton University Distinguished Professor Jessica Fridrich.

Credit: Jonathan Cohen / Binghamton University

For more than 25 years, Binghamton University Distinguished Professor Jessica Fridrich has studied digital-image steganography — the science of hiding messages inside ordinary-looking photos.

Just as technology has evolved and become more sophisticated, so have the methods to share secrets — and a recent $768,964 grant from the U.S. National Science Foundation will help Fridrich stay ahead of the curve.

"This is . . . something I've been doing for a long time — but it's still relevant," says Fridrich, a faculty member in the Department of Electrical and Computer Engineering at Binghamton University's Thomas J. Watson College of Engineering and Applied Science. "There are still many open questions in the field."

At the heart of steganography is the idea that someone can look at a file, message, image, or video and not realize that there is anything hidden.

Digital files — particularly ones for audio, videos, or photos — are considered ideal for steganographic transmission because of their large size. The need for such technology goes beyond the kind of espionage to homeland security, military planning, and protecting the privacy of citizens.

"As a user of the system, you want to know you can hide a message that has a certain length or size, and you don't want to worry about being detected," Fridrich says.

The NSF project will study the principles and limits of stealth digital communication by starting with a detailed mathematical understanding of how a digital image is formed inside of a camera. That model — developed through statistical hypothesis, deep learning, deep neural networks, pixel analysis, and more — would provide users a provable assurance of how secure a particular steganographic system is.

From Binghamton University
View Full Article


 

No entries found