A New Way to Detect Digital Child Pornography?

April 14, 2010— -- The fight against child pornography could be getting a new high-tech tool.

To help law enforcement with the task of analyzing a suspected child pornographer's computer, new software developed by a computer science professor at the Polytechnic Institute of New York University brings deleted photographs back from the computer's trash and searches them for potentially explicit images of children and differentiates them from images of adult.

The program scans for faces of children, nudity and other features to help flag images that could possibly be illegal contraband.

"It uses machine-learning algorithms to distinguish child from not-child," said Nasir Memon, a professor of computer science who created the program with his students.

"Machine learning" refers to a process by which a program learns to identify certain kinds of images by processing other similar images.

"[The program looks] at the face, skin, nudity, other features potentially that combine together, to pull out the most likely images which could be problematic," he said.

The program was designed to help law enforcement, defense and intelligence officials and private investigators hired by the private sector, Memon said.

As law enforcement grapples with increasing volumes of digital child porn, child advocates say technology that can help streamline the identification process is becoming more valuable.

"It is a struggle for law enforcement who are working child porn cases," said Michelle Collins, vice president of the National Center for Missing and Exploited Children's exploited children division. "Over the last few years, the size of the child porn being seized has increased dramatically."

Memon said the program computes the distance between a person's eyes and nose and other facial features to separate children from adults, but low light and non-frontal photos can skew results.

The program is about 70 percent accurate in identifying images of children, Memon said, but he added that even that success rate could be helpful in narrowing the field for investigators building a case against a child porn suspect.

Through Digital Assembly, a Brooklyn, N.Y.-based start-up created by Memon and two of his students, he previously released a version of the software that recovers deleted and fragmented digital images. The newest version of the software, called Adroit, will launch later this month and includes the new filtering technology.

In addition to scanning for potentially explicit images of children, he said the program can search for explicit images of adults, photographs of a particular person and indoor or outdoor photos.