edwardraffml.bsky.social

Philip Doldo 's first @cvprconference.bsky.social paper, 𝑆𝑡𝑜𝑝 𝑊𝑎𝑙𝑘𝑖𝑛𝑔 𝑖𝑛 𝐶𝑖𝑟𝑐𝑙𝑒𝑠! 𝐵𝑎𝑖𝑙𝑖𝑛𝑔 𝑂𝑢𝑡 𝐸𝑎𝑟𝑙𝑦 𝑖𝑛 𝑃𝑟𝑜𝑗𝑒𝑐𝑡𝑒𝑑 𝐺𝑟𝑎𝑑𝑖𝑒𝑛𝑡 𝐷𝑒𝑠𝑐𝑒𝑛𝑡, is a rare free lunch for 10x faster AML 🧵👇 openaccess.thecvf.com/content/CVPR...

This results in real-world speedups; there are two slightly slower cases because the models just didn't cycle. But those are on CIFAR10, as the dimension goes up we see better results because more of the mass exists on the edge of the ∞-norm ball, making cycling easier.

Philip Doldo 's first @cvprconference.bsky.social paper, 𝑆𝑡𝑜𝑝 𝑊𝑎𝑙𝑘𝑖𝑛𝑔 𝑖𝑛 𝐶𝑖𝑟𝑐𝑙𝑒𝑠! 𝐵𝑎𝑖𝑙𝑖𝑛𝑔 𝑂𝑢𝑡 𝐸𝑎𝑟𝑙𝑦 𝑖𝑛 𝑃𝑟𝑜𝑗𝑒𝑐𝑡𝑒𝑑 𝐺𝑟𝑎𝑑𝑖𝑒𝑛𝑡 𝐷𝑒𝑠𝑐𝑒𝑛𝑡, is a rare free lunch for 10x faster AML 🧵👇 openaccess.thecvf.com/content/CVPR...

A second simple observation is that when you do successfully attack an image, you can just stop. Shockingly, no code for PGD in any of the major frameworks checks for this! Once in a cycle you know the future, so also stop. So the complete procedure is very simple.

Philip Doldo 's first @cvprconference.bsky.social paper, 𝑆𝑡𝑜𝑝 𝑊𝑎𝑙𝑘𝑖𝑛𝑔 𝑖𝑛 𝐶𝑖𝑟𝑐𝑙𝑒𝑠! 𝐵𝑎𝑖𝑙𝑖𝑛𝑔 𝑂𝑢𝑡 𝐸𝑎𝑟𝑙𝑦 𝑖𝑛 𝑃𝑟𝑜𝑗𝑒𝑐𝑡𝑒𝑑 𝐺𝑟𝑎𝑑𝑖𝑒𝑛𝑡 𝐷𝑒𝑠𝑐𝑒𝑛𝑡, is a rare free lunch for 10x faster AML 🧵👇 openaccess.thecvf.com/content/CVPR...

The crux of the paper is simple, you want a large max iteration count T, but you don't want ot use them all every time! When a model is robust under the L∞ norm, your perturbation starts to cycle in the corners, so you start to do redundant work!

Would you like to have the world's most accurate malware label predictor? RJ #BoozAllen has you covered w/ ClarAVy: A Tool for Scalable and Accurate Malware Family Labeling, arxiv.org/abs/2502.02759 work 🧵👇 to appear at thewebconf.org

But plurality voting like everyone else is lame. We want to be Bayesian about it, and scale up Independent Bayesian Classifier Combination to handle sparse data like we have - with the world's fastest and most scalable implementation!

Would you like to have the world's most accurate malware label predictor? RJ #BoozAllen has you covered w/ ClarAVy: A Tool for Scalable and Accurate Malware Family Labeling, arxiv.org/abs/2502.02759 work 🧵👇 to appear at thewebconf.org

You start with AV reports from your friendly neighborhood @virustotal.bsky.social , but you get conflicting answers /info that is unstandardized from every AV, what do you do?

Don't know why @cvprconference.bsky.social meta-reviews took a bit to come up, but ❤️ mine!: "The reviewers have mixed feelings about this paper. They generally agree that: (1) the work described here has practical value (as it allows to speed up considerably the well-known PGD algorithm), and

(2) the proposed method is very simple. Besides other minor issues, disagreement arises as to whether a very simple but useful idea, as the one proposed here, deserves being published in a prestigious scientific venue such as CVPR. We discussed this extensively during the AC triplet meeting and we

I'm currently working through/implementing the Hager Zhang line search b/c I need it for some research, and it's a monster! Would anyone care if I made a tech report on "so you want to implement LBFGs/CG just give me the details"? B/c its really not well documented anywhere.

AV system is hard in part b/c you are constantly searching for new features. But analysts are also writing Yara rules, can you steal them as features? New paper #IEEEBigData looks are how you can live of the analyst w/ @cmat.bsky.social @cknicholas.bsky.social @csee-umbc.bsky.social 🧵

And voila, you can outperform Ember-only features by adding features selected by mining Yara rules! It is a fairly non-trivial lift, considering how little effort was involved and that you don't need to add that many Yara atoms to get a lift.

AV system is hard in part b/c you are constantly searching for new features. But analysts are also writing Yara rules, can you steal them as features? New paper #IEEEBigData looks are how you can live of the analyst w/ @cmat.bsky.social @cknicholas.bsky.social @csee-umbc.bsky.social 🧵

Training on only the Yara signatures as features is surprisingly decent as a malware detector! Not amazing, but decent! But the real value here is that they are being created/updated already - and you can add them to your existing feature sets.

AV system is hard in part b/c you are constantly searching for new features. But analysts are also writing Yara rules, can you steal them as features? New paper #IEEEBigData looks are how you can live of the analyst w/ @cmat.bsky.social @cknicholas.bsky.social @csee-umbc.bsky.social 🧵

Yara rules are basically fancy regular expressions. We can take all the 'atoms' of the rule, like $av4, and turn them into binary present/absent features for our model to incorporate. Nature of Yara means these are usually pretty fast to find/extract too 📰https://arxiv.org/abs/2411.18516