[HN Gopher] Show HN: Lava lamp simulated by neural net in infini...
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       Show HN: Lava lamp simulated by neural net in infinite loop
        
       duralava is a neural network which can simulate a lava lamp in an
       infinite loop.  It uses a recurrent GAN that learns the physical
       behavior of the lava lamp.  A noteworthy aspect is that can
       generate an arbitrarily long video of a (virtual) lava lamp,
       without diverging even after thousands of frames.
        
       Author : muxamilian
       Score  : 48 points
       Date   : 2022-02-05 15:52 UTC (7 hours ago)
        
 (HTM) web link (github.com)
 (TXT) w3m dump (github.com)
        
       | uoaei wrote:
       | Maybe I am confused. Is it simulating a lava lamp, or a video of
       | a lava lamp?
        
       | anu7df wrote:
       | If i am remembering correctly, there was a company that was using
       | videos of lava lamps for encryption or as passwords or some such.
       | The claim was that it is un crackable because truly random. I
       | wonder if this can be used to emulate the physical process and
       | break that encryption.
        
         | muxamilian wrote:
         | Cloudflare does that: https://www.cloudflare.com/en-
         | gb/learning/ssl/lava-lamp-encr...
         | 
         | That's an interesting aspect, which I haven't thought of. I
         | think real world lava lamps have very chaotic behavior. I think
         | the neural network, however, just learns the most common
         | behaviors of a lava lamp but so far cannot learn every aspect
         | of a lava lamp. Training a bigger neural network could work
         | though...
        
         | btrettel wrote:
         | Similating a Lava lamp with ML doesn't affect the security of
         | RNGs in my view. You could do a computer simulation of a Lava
         | lamp with the Navier-Stokes equations for a long time now.
         | Chaos theory would mean that predicting the long-term future of
         | a particular realization would require extremely high precision
         | measurements of the initial conditions and boundary conditions
         | of the lamp, certainly beyond what is feasible and possibly
         | beyond what is possible. ML doesn't change that fact. It is
         | possible, however, to predict statistics but that wouldn't be
         | useful to break encryption.
        
         | [deleted]
        
         | webmaven wrote:
         | _> If i am remembering correctly, there was a company that was
         | using videos of lava lamps for encryption or as passwords or
         | some such._
         | 
         | The method dates back to SGI's Lavarand:
         | https://en.m.wikipedia.org/wiki/Lavarand
         | 
         | A related system, LavaRnd uses webcams that have their lenscaps
         | on, so the sensors are only detecting thermal noise:
         | 
         | https://www.lavarand.org/
         | 
         | But Cloudflare is the most famous implementor of the technique:
         | 
         | https://blog.cloudflare.com/randomness-101-lavarand-in-produ...
         | 
         | ISTR that US embassies tried using atmospheric noise as a seed
         | for generating one-time pads at some point, but that was
         | deprecated as being too vulnerable to undetectable outside
         | interference.
         | 
         | By comparison, the Cloudflare system with the lava lamps in the
         | lobby is tamper-evident.
         | 
         |  _> The claim was that it is un crackable because truly random.
         | I wonder if this can be used to emulate the physical process
         | and break that encryption._
         | 
         | No. Chaotic processes are so sensitive to initial conditions
         | and perturbations from the environment that any simulation
         | quickly diverges from the actual process being simulated. Other
         | common macroscale systems that exhibit this property are the
         | three body problem and double pendulums.
        
         | gus_massa wrote:
         | Note that this method generate a video that looks nice and is
         | very similar to a lava lamp, but it's not a 100% pixel perfect
         | simulation of a lava lamp.
         | 
         | I'm not sure if you can use an image or short video as a seed,
         | but in case it's possible, the rest of the real video and the
         | rest of the video generated by this will look similar for a
         | short time, but after a while they will be more and more
         | different.
         | 
         | It's similar to turning on two lamps of the same factory at the
         | same time. In spite they look similar initially, after a while
         | they will look different.
         | 
         | In particular a real lamp will get some vibration the cars in
         | the streets that will affect the content just a little, but
         | after a while the chaotic behavior will make the differences
         | bigger. Also small temperature differences from the sunlight
         | from the window, and other stuff that looks unimportant will
         | cause the real lamp to have a unpredictable behavior after some
         | time.
        
         | thunderbong wrote:
         | Past threads -
         | 
         | Cloudflare generating Pseudo-random numbers from 100 lava lamps
         | (4 years ago - 4 comments - gizmodo.com)
         | 
         | https://news.ycombinator.com/item?id=15639320
         | 
         | Lavarand - Hardware random number generator using lava lamps
         | (11 years ago - 0 comments - wikipedia.org)
         | 
         | https://news.ycombinator.com/item?id=15639320
         | 
         | Relevant website -
         | 
         | LavaRnd
         | 
         | http://www.lavarnd.org/lavarnd.html
        
         | pavlov wrote:
         | I remember hearing this in the late '90s, and the company using
         | "camera pointed at lava lamp" as a random number seed was SGI.
        
       | isoprophlex wrote:
       | Fun project, even though I personally find the gan images
       | unconvincing; too many deconvolution artifacts, and poor
       | conservation of mass as another commenter said.
       | 
       | I am however completely awed by the folder in that git repo with
       | 143.000 png files. Checking that into git would have turned my
       | laptop itself into a molten blob of wax, haha.
       | 
       | Edit: rereading my comment, maybe it sounds harsh. Idon't wanna
       | sound like I'm dissing this, GANs are hard and so is image
       | generation. I couldn't have done it better.
       | 
       | Also: nice trick on penalizing poor (growing) noise vectors;
       | another thing you could try is simply always sample a random
       | point on an n-sphere (you divide your random vector by its
       | length, it'll always have length 1)
        
       | Moosdijk wrote:
       | It does not do it convincingly. At least the gifs on github show
       | blobs of lava disappearing mid rise.
       | 
       | It's nice work though.
        
         | klyrs wrote:
         | Maybe it's just me, but conservation of mass seems like
         | something you'd want to hard-code, not learn.
        
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       (page generated 2022-02-05 23:00 UTC)