Convert Apple NeuralHash model for CSAM Detection to ONNX. (by AsuharietYgvar)

AppleNeuralHash2ONNX Alternatives

Similar projects and alternatives to AppleNeuralHash2ONNX

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a better AppleNeuralHash2ONNX alternative or higher similarity.

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Reviews and mentions

Posts with mentions or reviews of AppleNeuralHash2ONNX. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-09-17.
  • Under pressure from Russian government Google, Apple remove opposition leader's Navalny app from stores as Russian elections begin | 2021-09-17
  • A wake-up call for iPhone users -- it's time to go | Free Software Foundation | 2021-09-09
    Apple says if you turn off iCloud photos it shouldn't scan your phone. But Apple is proprietary and there isn't a way for anyone or group to verify what Apple says. A developer did some reverse engineering and found some NeuralHash code that is being used for CSAM in iOS from 14.3. So as usual all we can do is take Apple's word for it.
  • Apple iPhone 13 or Google Pixel 6? | 2021-09-09
  • EFF Pressures Apple to Completely Abandon Controversial Child Safety Features | 2021-09-08
    Well, it’s a good thing Apple’s algorithm to check for CSAM hasn’t been found to be broken, so I’m sure we can trust it to be accurate.
  • Delays Aren't Good Enough—Apple Must Abandon Its Surveillance Plans | 2021-09-04
    People have already engineered collisions for this system lol. It took like two weeks after it was announced. | 2021-09-04
  • 25,000 EFF Supporters Have Told Apple Not To Scan Their Phones | 2021-09-03
    Even if we make the wild assumption that Apple's systems cannot be hacked nor that an employee there will ever leak it, hackers can reverse engineer it. Even the first collisions have been found.
  • Apple delays plans to roll out CSAM detection in iOS 15 | 2021-09-03
    In fact, researchers were quickly able to create their own collisions from two separate images. Thus, they show that NeuralHash is not nearly as immune from collisions, as Apple let on, in the first place. Source. | 2021-09-03
  • Interest in Switching to iPhone Drops Among Android Users Ahead of iPhone 13 Launch, Survey Shows | 2021-08-31
    Let's say the TSA installed security checkpoints at your house's door - any time you left, you were scanned with x-ray. They promise that they will only look at these xrays if a very falliable algorithm detects multiple bombs in your bag.
  • A Screeching Voice of the Minority | 2021-08-30
    > I don't think you could just have a completely different picture create a collision though.

    Allow me to introduce you to my posts on github:

    Where I post good looking examples of standard test images altered fairly subtly to give the specific hashes.

    The apple neuralhash is broken as a 'hash function'.

    It's much much easier to modify images to just have a different hash. A simple blemish on the image-- or, with whiteboxing using the hash function, no visually noticeable change is required at all.

  • Show HN: 59a34eabe31910abfb06f308 NeuralHash Collision Demo | 2021-08-25
    Neuralhash is a neural network. This means that its locally differentiable.

    To make an image match a specific hash, you pass the you want to modify image through neuralhash and compute a difference with your target hash, then ask your netural network library to use reverse mode automatic differentiation to give you the gradients for each of the outputs with respect to the input pixels.

    Update the input pixels.

    To make the collisions that look good, you need to either augment the objective to include some 'good looking' metric, or condition your gradient descent to prefer good looking results.

    In my examples ( ) I used a gaussian blur to highpass the error between my starting image and the current work in progress, and fed that back in, and adapted the feedback to be as great as possible while still keeping it colliding.

    The bad looking examples that people initially posted just did nothing to try to make the result look good.

    I'm thrilled to see that someone other than me has now also worked out getting fairly reasonable looking examples.

  • A catalog of naturally occurring images whose Apple NeuralHash is identical | 2021-08-25
    > it is predicated on the assumption that you can easily mangle pictures to NeuralHash-collide with a desired target picture (out of a set of widely circulating innocuous pictures) without deteriorating the visual content too much.

    You can. Here is an example I created (with links to more):

    I'm so tired of people suggesting that you can't. Please explain to me why you posted suggesting otherwise. | 2021-08-25
    Why are exact collisions interesting?

    This algorithm doesn't even give exact matches for the same image on different hardware.

    Note: Neural hash generated here might be a few bits off from one generated on an iOS device. This is expected since different iOS devices generate slightly different hashes anyway. The reason is that neural networks are based on floating-point calculations. The accuracy is highly dependent on the hardware. For smaller networks it won't make any difference. But NeuralHash has 200+ layers, resulting in significant cumulative errors.

  • Apple's Neural Hash Algorithm is Collision-Proof!


Basic AppleNeuralHash2ONNX repo stats
about 1 month ago

AsuharietYgvar/AppleNeuralHash2ONNX is an open source project licensed under Apache License 2.0 which is an OSI approved license.

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