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The sample set is ImageNet, which is a well-known dataset in Computer Vision and is available for download here: https://www.kaggle.com/c/imagenet-object-locali

The sample set is ImageNet, which is a well-known dataset in Computer Vision and is available for download here: https://www.kaggle.com/c/imagenet-object-localization-challenge https://www.kaggle.com/c/imagenet-object-localization-challe... I'd love to see this work extended; if you find additional collisions in the wild please submit a PR to the repo (please do not submit artificially generated adversarial images): https://github.com/roboflow-ai/neuralhash-collisions https://github.com/roboflow-ai/neuralhash-collisions For what it's worth, Apple claimed to find a _lower_ incidence of false-positives when it used pornographic images in its test[1] (which makes sense; images containing humans is probably more aligned with what the model was trained on than nematodes) [1] https://tidbits.com/2021/08/13/new-csam-detection-details-emerge-following-craig-federighi-interview/ https://tidbits.com/2021/08/13/new-csam-detection-details-em... > In Apple’s tests against 100 million non-CSAM images, it encountered 3 false positives when compared against NCMEC’s database. In a separate test of 500,000 adult pornography images matched against NCMEC’s database, it found no false positives.

smithza 5y ago
For what it's worth, Apple claimed to find a _lower_ incidence of false-positives when it used pornographic images in its test[1] (which makes sense; images containing humans is probably more aligned with what the model was trained on than nematodes) This is an important note. Is it the case that this algorithm is trained for humans or not? the 1/trillion false-positive rate might imply it is trained with a broader set. Thank you for those helpful tidbits.
tgsovlerkhgsel 5y ago
0 out of 0.5 million vs. 3 out of 100 million does not imply with any reasonable confidence that the incidence in porn is lower. You'd expect the same result even if the incidence in porn was 10x higher than in typical images (30 in 100 million = 0.15 in 0.5 million).
fallingknife 5y ago
500K is not a large enough dataset to determine that. The collision rate could plausibly be 1 in 500K (or even a bit higher) and have no collisions in the sample.
contravariant 5y ago
Well to know for sure if that's lower we'd need to know the size of the NCMEC database. They're the same if the NCMEC contains around 10 000 images. Though knowing that 1 in roughly 30 million images generates a false positive is the most important figure I suppose. Assuming 100 million iPhones with each 1000 pictures that would generate some 3000 phones with one or more false positives [1] and a roughly 5% chance that some phone has at least 2 false positives. [1]: https://www.wolframalpha.com/input/?i=%28100+million%29+e%5E-l+%28e%5El+-+1%29+where+l+%3D+%281000.0%2F30+million%29 https://www.wolframalpha.com/input/?i=%28100+million%29+e%5E... [2]: https://www.wolframalpha.com/input/?i=+%281+-+e%5E-l+%28e%5El+-+1+-+l%29%29+%5E+%28100+million%29+where+l+%3D+%281000.0%2F30+million%29 https://www.wolframalpha.com/input/?i=+%281+-+e%5E-l+%28e%5E...

smithza 5y ago

For what it's worth, Apple claimed to find a _lower_ incidence of false-positives when it used pornographic images in its test[1] (which makes sense; images containing humans is probably more aligned with what the model was trained on than nematodes) This is an important note. Is it the case that this algorithm is trained for humans or not? the 1/trillion false-positive rate might imply it is trained with a broader set. Thank you for those helpful tidbits.

tgsovlerkhgsel 5y ago

0 out of 0.5 million vs. 3 out of 100 million does not imply with any reasonable confidence that the incidence in porn is lower. You'd expect the same result even if the incidence in porn was 10x higher than in typical images (30 in 100 million = 0.15 in 0.5 million).

fallingknife 5y ago

500K is not a large enough dataset to determine that. The collision rate could plausibly be 1 in 500K (or even a bit higher) and have no collisions in the sample.

contravariant 5y ago

Well to know for sure if that's lower we'd need to know the size of the NCMEC database. They're the same if the NCMEC contains around 10 000 images. Though knowing that 1 in roughly 30 million images generates a false positive is the most important figure I suppose. Assuming 100 million iPhones with each 1000 pictures that would generate some 3000 phones with one or more false positives [1] and a roughly 5% chance that some phone has at least 2 false positives. [1]: https://www.wolframalpha.com/input/?i=%28100+million%29+e%5E-l+%28e%5El+-+1%29+where+l+%3D+%281000.0%2F30+million%29 https://www.wolframalpha.com/input/?i=%28100+million%29+e%5E... [2]: https://www.wolframalpha.com/input/?i=+%281+-+e%5E-l+%28e%5El+-+1+-+l%29%29+%5E+%28100+million%29+where+l+%3D+%281000.0%2F30+million%29 https://www.wolframalpha.com/input/?i=+%281+-+e%5E-l+%28e%5E...