Entries Tagged "video"

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Details of a Computer Banking Scam

This is a longish video that describes a profitable computer banking scam that’s run out of call centers in places like India. There’s a lot of fluff about glitterbombs and the like, but the details are interesting. The scammers convince the victims to give them remote access to their computers, and then that they’ve mistyped a dollar amount and have received a large refund that they didn’t deserve. Then they convince the victims to send cash to a drop site, where a money mule retrieves it and forwards it to the scammers.

I found it interesting for several reasons. One, it illustrates the complex business nature of the scam: there are a lot of people doing specialized jobs in order for it to work. Two, it clearly shows the psychological manipulation involved, and how it preys on the unsophisticated and vulnerable. And three, it’s an evolving tactic that gets around banks increasingly flagging blocking suspicious electronic transfers.

Posted on March 22, 2021 at 6:15 AMView Comments

Determining What Video Conference Participants Are Typing from Watching Shoulder Movements

Accuracy isn’t great, but that it can be done at all is impressive.

Murtuza Jadiwala, a computer science professor heading the research project, said his team was able to identify the contents of texts by examining body movement of the participants. Specifically, they focused on the movement of their shoulders and arms to extrapolate the actions of their fingers as they typed.

Given the widespread use of high-resolution web cams during conference calls, Jadiwala was able to record and analyze slight pixel shifts around users’ shoulders to determine if they were moving left or right, forward or backward. He then created a software program that linked the movements to a list of commonly used words. He says the “text inference framework that uses the keystrokes detected from the video … predict[s] words that were most likely typed by the target user. We then comprehensively evaluate[d] both the keystroke/typing detection and text inference frameworks using data collected from a large number of participants.”

In a controlled setting, with specific chairs, keyboards and webcam, Jadiwala said he achieved an accuracy rate of 75 percent. However, in uncontrolled environments, accuracy dropped to only one out of every five words being correctly identified.

Other factors contribute to lower accuracy levels, he said, including whether long sleeve or short sleeve shirts were worn, and the length of a user’s hair. With long hair obstructing a clear view of the shoulders, accuracy plummeted.

Posted on November 4, 2020 at 10:28 AMView Comments

Sidebar photo of Bruce Schneier by Joe MacInnis.