text: 253,30,1164665133890592768,1137569442404036609,Daniel Liu,"New paper 📰: Adversarial point perturbations on 3D objects <LINK> Blog post: <LINK> *FOUR* novel #adversarial attacks against #3D #pointcloud #DeepLearning! #security #MachineLearning 1/10 <LINK> Thanks to Ronald Yu and Hao Su from @UCSanDiego for minimally mentoring me and providing GPUs even though I am a high school student! This paper would not be possible without previous work by @goodfellow_ian @aleks_madry @NicolasPapernot @alexey2004 and many others! 2/10 Idea 💡: shape-aware attacks with *just* unordered point sets. Two categories: - Distributional: imperceptible; measures perturbation through Hausdorff metric. - Shape: global changes to shape; realistic and robust against point removal defenses. 3/10 <LINK> Distributional attack: estimate shape of point set and use projected gradient descent to keep perturbations on the shape. Shape estimation uses triangulation algorithms, and projection is sped up with a metric tree. 4/10 <LINK> For reference, directly using gradient descent results in many outlier points floating around in mid-air: 5/10 <LINK> Perturbation resampling attack: gradient descent + resampling points onto shape inferred through triangulation to maintain an even sampling of points on the shape. 6/10 <LINK> Adversarial sticks: perturb a few points and connect them to triangulated shape by resampling points, instead of the harder task of orienting sticks in 3D. This is easier to construct IRL. 7/10 <LINK> Adversarial sinks: instead of perturbing single points, move ""black holes"" that attract points based on a radial basis function to change the shape. This is fully differentiable. 8/10 <LINK> Shape attacks perform much better than naive gradient descent (iter. gradient L_2) even when we remove up to half of the points (using previously proposed algorithms) as a defense. 9/10 <LINK> Please read the blog post (<LINK>) for a brief history of ideas in the field of point set adversarial attacks. 10/10 @UCSanDiego @goodfellow_ian @aleks_madry @NicolasPapernot @alexey2004 Also @ChrSzegedy for the first paper on adversarial examples!",https://arxiv.org/abs/1908.06062,"The importance of training robust neural network grows as 3D data is increasingly utilized in deep learning for vision tasks in robotics, drone control, and autonomous driving. One commonly used 3D data type is 3D point clouds, which describe shape information. We examine the problem of creating robust models from the perspective of the attacker, which is necessary in understanding how 3D neural networks can be exploited. We explore two categories of attacks: distributional attacks that involve imperceptible perturbations to the distribution of points, and shape attacks that involve deforming the shape represented by a point cloud. We explore three possible shape attacks for attacking 3D point cloud classification and show that some of them are able to be effective even against preprocessing steps, like the previously proposed point-removal defenses. ",Adversarial shape perturbations on 3D point clouds,11,"['New paper 📰:\nAdversarial point perturbations on 3D objects\n<LINK>\n\nBlog post: <LINK>\n\n*FOUR* novel #adversarial attacks against #3D #pointcloud #DeepLearning!\n\n#security #MachineLearning\n\n1/10 <LINK>', 'Thanks to Ronald Yu and Hao Su from @UCSanDiego for minimally mentoring me and providing GPUs even though I am a high school student!\n\nThis paper would not be possible without previous work by @goodfellow_ian @aleks_madry @NicolasPapernot @alexey2004 and many others!\n\n2/10', 'Idea 💡: shape-aware attacks with *just* unordered point sets.\n\nTwo categories:\n- Distributional: imperceptible; measures perturbation through Hausdorff metric.\n- Shape: global changes to shape; realistic and robust against point removal defenses.\n\n3/10 https://t.co/EKOSqIN1qO', 'Distributional attack: estimate shape of point set and use projected gradient descent to keep perturbations on the shape.\n\nShape estimation uses triangulation algorithms, and projection is sped up with a metric tree.\n\n4/10 https://t.co/nf3YqF5mkD', 'For reference, directly using gradient descent results in many outlier points floating around in mid-air:\n\n5/10 https://t.co/tcMVqQYSTx', 'Perturbation resampling attack: gradient descent + resampling points onto shape inferred through triangulation to maintain an even sampling of points on the shape.\n\n6/10 https://t.co/V73fsEiUWk', 'Adversarial sticks: perturb a few points and connect them to triangulated shape by resampling points, instead of the harder task of orienting sticks in 3D. This is easier to construct IRL.\n\n7/10 https://t.co/87ZqfUFGnc', 'Adversarial sinks: instead of perturbing single points, move ""black holes"" that attract points based on a radial basis function to change the shape. This is fully differentiable.\n\n8/10 https://t.co/hYzl6eOoou', 'Shape attacks perform much better than naive gradient descent (iter. gradient L_2) even when we remove up to half of the points (using previously proposed algorithms) as a defense.\n\n9/10 https://t.co/Yj5aItwIYV', 'Please read the blog post (https://t.co/FPbzLsT2Op) for a brief history of ideas in the field of point set adversarial attacks.\n\n10/10', '@UCSanDiego @goodfellow_ian @aleks_madry @NicolasPapernot @alexey2004 Also @ChrSzegedy for the first paper on adversarial examples!']",19,08,2081