2019-07-18: Natural Adversarial Examples https://arxiv.org/abs/1907.07174v2In this work, we identified several methods that increase natural adversarial example robustness, demonstrated that models have similar blindspots and make correlated errors, and created a hard new ImageNet test set to measure model robustness—an important research aim as computer vision systems are deployed in increasingly precarious environments.
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We introduce natural adversarial examples -- real-world, unmodified, and
naturally occurring examples that cause classifier accuracy to significantly
degrade. We curate 7,500 natural adversarial examples and release them in an
ImageNet classifier test set that we call ImageNet-A. This dataset serves as a
new way to measure classifier robustness. Like l_p adversarial examples,
ImageNet-A examples successfully transfer to unseen or black-box classifiers.
For example, on ImageNet-A a DenseNet-121 obtains around 2% accuracy, an
accuracy drop of approximately 90%. Recovering this accuracy is not simple
because ImageNet-A examples exploit deep flaws in current classifiers including
their over-reliance on color, texture, and background cues. We observe that
popular training techniques for improving robustness have little effect, but we
show that some architectural changes can enhance robustness to natural
adversarial examples. Future research is required to enable robust
generalization to this hard ImageNet test set.
2019-07-17: Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics https://arxiv.org/abs/1907.07296v1In addition, since currently our work only considers classification models for generalpurpose tasks, another extension would be to specialize our framework to support domain-specific analyses, such as image recognition, biological analysis, and network intrusion detection
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Machine learning models are currently being deployed in a variety of
real-world applications where model predictions are used to make decisions
about healthcare, bank loans, and numerous other critical tasks. As the
deployment of artificial intelligence technologies becomes ubiquitous, it is
unsurprising that adversaries have begun developing methods to manipulate
machine learning models to their advantage. While the visual analytics
community has developed methods for opening the black box of machine learning
models, little work has focused on helping the user understand their model
vulnerabilities in the context of adversarial attacks. In this paper, we
present a visual analytics framework for explaining and exploring model
vulnerabilities to adversarial attacks. Our framework employs a multi-faceted
visualization scheme designed to support the analysis of data poisoning attacks
from the perspective of models, data instances, features, and local structures.
We demonstrate our framework through two case studies on binary classifiers and
illustrate model vulnerabilities with respect to varying attack strategies.
Discovering good libraries and code might be overwhelming. - our Ecosystem page has been curating high quality libraries - Hub has been curating models that reproduce research and are easy to use Read more about our recent additions and guidelines: [pytorch.org][twitter] @PyTorch
Discovering good libraries and code might be overwhelming.
- our Ecosystem page has been curating high quality libraries
- Hub has been curating models that reproduce research and are easy to use
Read more about our recent additions and guidelines: https://pytorch.org/blog/pytorch-ecosystem/…pic.twitter.com/oETW02dQ6y
2019-07-15: Real-time Hair Segmentation and Recoloring on Mobile GPUs https://arxiv.org/abs/1907.06740v1Our ML pipeline can be executed at higher than realtime speeds, achieving a realistic AR hair recoloring effect on mobile phones, as shown in
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We present a novel approach for neural network-based hair segmentation from a
single camera input specifically designed for real-time, mobile application.
Our relatively small neural network produces a high-quality hair segmentation
mask that is well suited for AR effects, e.g. virtual hair recoloring. The
proposed model achieves real-time inference speed on mobile GPUs (30-100+ FPS,
depending on the device) with high accuracy. We also propose a very realistic
hair recoloring scheme. Our method has been deployed in major AR application
and is used by millions of users.
Descartes on the limits of Artificial Intelligence: "Even though such machines might do some things as well as we do them, or perhaps even better, they would inevitably fail in others, which would reveal they were acting not through understanding" (Discourse on the Method, 1637)
2019-07-15: Batch-Shaped Channel Gated Networks https://arxiv.org/abs/1907.06627v1In both datasets, given a model with large capacity, our gating method was the only approach that could reduce the inference computation to a value equal or lower than that of a lower capacity base network, while obtaining a higher accuracy
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We present a method for gating deep-learning architectures on a fine-grained
level. Individual convolutional maps are turned on/off conditionally on
features in the network. This method allows us to train neural networks with a
large capacity, but lower inference time than the full network. To achieve
this, we introduce a new residual block architecture that gates convolutional
channels in a fine-grained manner. We also introduce a generally applicable
tool "batch-shaping" that matches the marginal aggregate posteriors of features
in a neural network to a pre-specified prior distribution. We use this novel
technique to force gates to be more conditional on the data. We present results
on CIFAR-10 and ImageNet datasets for image classification and Cityscapes for
semantic segmentation. Our results show that our method can slim down large
architectures conditionally, such that the average computational cost on the
data is on par with a smaller architecture, but with higher accuracy. In
particular, our ResNet34 gated network achieves a performance of 72.55% top-1
accuracy compared to the 69.76% accuracy of the baseline ResNet18 model, for
similar complexity. We also show that the resulting networks automatically
learn to use more features for difficult examples and fewer features for simple
examples.
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The ability to detect objects regardless of image distortions or weather
conditions is crucial for real-world applications of deep learning like
autonomous driving. We here provide an easy-to-use benchmark to assess how
object detection models perform when image quality degrades. The three
resulting benchmark datasets, termed Pascal-C, Coco-C and Cityscapes-C, contain
a large variety of image corruptions. We show that a range of standard object
detection models suffer a severe performance loss on corrupted images (down to
30-60% of the original performance). However, a simple data augmentation trick
- stylizing the training images - leads to a substantial increase in robustness
across corruption type, severity and dataset. We envision our comprehensive
benchmark to track future progress towards building robust object detection
models. Benchmark, code and data are available at: http://github.com/bethgelab/robust-detection-benchmark
Facebook asks users to add their phone numbers for 2-factor authentication, then misuses that info to target ads. Researchers at Northeastern uncovered this by signing up as an advertiser and finding many clever tricks to reverse-engineer FB's targeting. [mislove.org][twitter] @random_walker
Facebook asks users to add their phone numbers for 2-factor authentication, then misuses that info to target ads. Researchers at Northeastern uncovered this by signing up as an advertiser and finding many clever tricks to reverse-engineer FB's targeting. https://mislove.org/publications/PII-PETS.pdf…pic.twitter.com/au5qZEGZrx
