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In my personal opinion, a naive gaussian blur could be susceptible to supersampling attacks, especially in a video where you can take samples over multiple frames. In a production implementation, I would consider adding some steps to mitigate this.
In my personal opinion, a naive gaussian blur could be susceptible to supersampling attacks, especially in a video where you can take samples over multiple frames. In a production implementation, I would consider adding some steps to mitigate this.
Theoretically, augmenting the blurred image with some sort of random, changing offset for the HSV values on each frame would prevent supersampling attacks, right?
If you didn't have any noise you could just simply invert the Gaussian blur, as blurring is a reversible operation (in theory, in practice you have noise, hence the need for supersampling).
Annoyingly it's hard to come up with a method that doesn't leak any information, although heavily pixelating the image (and optionally blurring the few leftover pixels) would probably make it infeasible to recover the original (except maybe with a truly stupendous amount of footage).
nice work. If you have GPU power, I recommend using tinyfaces ( https://www.cs.cmu.edu/~peiyunh/tiny/ ) as your face detector. But you won't get realtime performance... We used a similar model to detect and blur faces from raw user uploaded images at scale, using a multi-stage pipeline.
Does it work on GPUs other than Nvidia?