This might work today, but it won’t work tomorrow. This is just one side of a GAN, on the next iteration, it will be defeated. Bottom line is that if a human can recognize, then it is possible for a machine as well. Also, given that the big networks can just keep throwing more resources at it (I.e. GPT-3), it’s just a matter of increasing the network size to improve feature redundancy.
Fawkes: Image “Cloaking” for Personal Privacy
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Re: Fawkes: Image “Cloaking” for Personal Privacy
#32I was immediately was curious how it would protect against image compression and/or otherwise de-noising these protection pixel changes. Their paper does address this question, and for those that are interested: Even image compression cannot defeat our cloak. We use progressive JPEG [57], reportedly used by Facebook and Twitter, to compress the images in our dataset. The image quality, as standard by Independent JPEG…
How about a 1px Gaussian blur? Haven't read the paper but I had the same thought. Seems way too tenuous.
> However,we find that none of these transformations defeat our cloaks. The protection success rate remains 100% even when data augmentation is applied to cloaked images5. Ap-plying Gaussian blurring degrades normal accuracy by up to18% (as kernel size increases) while cloak protection success rate remains>98% (see Figure13). Adding Gaussian noise to images merely disrupts normal classification accuracy –the cloak protection success rate remains above 100% as the standard deviation of the noise distribution increases(seeFigure14). Even image compression cannot defeat our cloak.We use progressive JPEG [57], reportedly used by Facebookand Twitter, to compress the images in our dataset. The im-age quality, as standard by Independent JPEG Group [1],ranges from 5 to 95 (lower value = higher compression). As shown in Figure15, image compression decreases the pro-tection success rate, but more significantly degrades normal classification accuracy.
Re: Fawkes: Image “Cloaking” for Personal Privacy
#33This might work today, but it won’t work tomorrow. This is just one side of a GAN, on the next iteration, it will be defeated. Bottom line is that if a human can recognize, then it is possible for a machine as well. Also, given that the big networks can just keep throwing more resources at it (I.e. GPT-3), it’s just a matter of increasing the network size to improve feature redundancy.
More accurately, if a human can accurately label inputs and measure outputs, it's possible for a machine. The human eye isn't the peak, just our current standard.
Re: Fawkes: Image “Cloaking” for Personal Privacy
#34Re: Fawkes: Image “Cloaking” for Personal Privacy
#35Re: Fawkes: Image “Cloaking” for Personal Privacy
#36Re: Fawkes: Image “Cloaking” for Personal Privacy
#37Color me extremely skeptical. A low-pass filter will make short work of any "tiny, pixel-level" changes designed to thwart ML. After all, one of the most tell-tale identifiers (space between eyes/nose/mouth) is still plainly observable and unaltered in the "cloaked" image. If a human's neural network can correctly correlate the before/after examples, so can a computer's. They might have found an issue with some moder…
Re: Fawkes: Image “Cloaking” for Personal Privacy
#38Re: Fawkes: Image “Cloaking” for Personal Privacy
#39This might work today, but it won’t work tomorrow. This is just one side of a GAN, on the next iteration, it will be defeated. Bottom line is that if a human can recognize, then it is possible for a machine as well. Also, given that the big networks can just keep throwing more resources at it (I.e. GPT-3), it’s just a matter of increasing the network size to improve feature redundancy.