1,130 research outputs found
Adversarial Scratches: Deployable Attacks to CNN Classifiers
A growing body of work has shown that deep neural networks are susceptible to
adversarial examples. These take the form of small perturbations applied to the
model's input which lead to incorrect predictions. Unfortunately, most
literature focuses on visually imperceivable perturbations to be applied to
digital images that often are, by design, impossible to be deployed to physical
targets. We present Adversarial Scratches: a novel L0 black-box attack, which
takes the form of scratches in images, and which possesses much greater
deployability than other state-of-the-art attacks. Adversarial Scratches
leverage B\'ezier Curves to reduce the dimension of the search space and
possibly constrain the attack to a specific location. We test Adversarial
Scratches in several scenarios, including a publicly available API and images
of traffic signs. Results show that, often, our attack achieves higher fooling
rate than other deployable state-of-the-art methods, while requiring
significantly fewer queries and modifying very few pixels.Comment: This paper stems from 'Scratch that! An Evolution-based Adversarial
Attack against Neural Networks' for which an arXiv preprint is available at
arXiv:1912.02316. Further studies led to a complete overhaul of the work,
resulting in this paper. This work was submitted for review in Pattern
Recognition (Elsevier
CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks
The unprecedented increase in the usage of computer vision technology in
society goes hand in hand with an increased concern in data privacy. In many
real-world scenarios like people tracking or action recognition, it is
important to be able to process the data while taking careful consideration in
protecting people's identity. We propose and develop CIAGAN, a model for image
and video anonymization based on conditional generative adversarial networks.
Our model is able to remove the identifying characteristics of faces and bodies
while producing high-quality images and videos that can be used for any
computer vision task, such as detection or tracking. Unlike previous methods,
we have full control over the de-identification (anonymization) procedure,
ensuring both anonymization as well as diversity. We compare our method to
several baselines and achieve state-of-the-art results.Comment: CVPR 202
CARPe Posterum: A Convolutional Approach for Real-time Pedestrian Path Prediction
Pedestrian path prediction is an essential topic in computer vision and video
understanding. Having insight into the movement of pedestrians is crucial for
ensuring safe operation in a variety of applications including autonomous
vehicles, social robots, and environmental monitoring. Current works in this
area utilize complex generative or recurrent methods to capture many possible
futures. However, despite the inherent real-time nature of predicting future
paths, little work has been done to explore accurate and computationally
efficient approaches for this task. To this end, we propose a convolutional
approach for real-time pedestrian path prediction, CARPe. It utilizes a
variation of Graph Isomorphism Networks in combination with an agile
convolutional neural network design to form a fast and accurate path prediction
approach. Notable results in both inference speed and prediction accuracy are
achieved, improving FPS considerably in comparison to current state-of-the-art
methods while delivering competitive accuracy on well-known path prediction
datasets.Comment: AAAI-21 Camera Read
TensorLayer: A Versatile Library for Efficient Deep Learning Development
Deep learning has enabled major advances in the fields of computer vision,
natural language processing, and multimedia among many others. Developing a
deep learning system is arduous and complex, as it involves constructing neural
network architectures, managing training/trained models, tuning optimization
process, preprocessing and organizing data, etc. TensorLayer is a versatile
Python library that aims at helping researchers and engineers efficiently
develop deep learning systems. It offers rich abstractions for neural networks,
model and data management, and parallel workflow mechanism. While boosting
efficiency, TensorLayer maintains both performance and scalability. TensorLayer
was released in September 2016 on GitHub, and has helped people from academia
and industry develop real-world applications of deep learning.Comment: ACM Multimedia 201
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