Blog - Adversarial Attack on Human Vision

Page / - last modified by Victor Zhang on 00:26, 22/07/2020

Adversarial Attack on Human Vision

last modified by Victor Zhang

on 00:26, 22/07/2020

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Shame on me wasting time on youtube again, it's me browsing through random videos and saw this

The human vision very much could be hacked like AI

However the given example, my hunch is that the picture is altered more than the computer AI Adversarial Attack example ( could totally be wrong about it, as I did not do objective verifiable calculation )

Based on my preception the picture (again totaly could be wrong), the noise they use changed several contour feature of the "cat" original picture, but that's only one example, probably will need to see the dataset compared to AI test.

Original paper is here: Adversarial Examples that Fool both Computer Vision and Time-Limited Humans

So the main takeaway of the study was not to fool human, it's the indication from time-limited human vs no time-limit human(robust) to infer there's potential for AI to be more robust.

Previous example for AI to be known vulnerable to adversarial attack is cited by Ref 13 also on same web site: EXPLAINING AND HARNESSING ADVERSARIAL EXAMPLES , "A demonstration of fast adversarial example generation applied to GoogLeNet (Szegedy et al., 2014a) on ImageNet", which shows the attack on panda image being wrongly recognized gibbon. However, I would argue this is only possible when they have control to finely rewrite input cache as 255*0.007 = 1.7 85, I doubt such grain could be picked up by real camera and even if so, would easily drown by noise of the sensor that even it accidently formed severl frames out of hundreds, in real application, the system could use statitstical method to make output more robust (Also I could be wrong), or the subtlety of modification is exaggerated.

However it never the less points out the flaw of the algorithm so that it could be improved upon.

There had been attempts to fool face recognition technology

The Privacy Wars: Goggles That Block Facial Recognition Technology

COUNTER SURVEILLANCE MAKEUP

Spoofing Faces Using Makeup: An Investigative Study

They are obvious, can only disable thre recognition, is it possible to project IR noise onto face to be recognized as someone else?
Like kinect's noise projection: kinect projected ir pattern, project calculated adversarial attack

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