161 research outputs found
Robustness of Adversarial Attacks in Sound Event Classification
An adversarial attack is a method to generate perturbations to the input of a machine learning model in order to make the output of the model incorrect. The perturbed inputs are known as adversarial examples. In this paper, we investigate the robustness of adversarial examples to simple input transformations such as mp3 compression, resampling, white noise and reverb in the task of sound event classification. By performing this analysis, we aim to provide insight on strengths and weaknesses in current adversarial attack algorithms as well as provide a baseline for defenses against adversarial attacks. Our work shows that adversarial attacks are not robust to simple input transformations. White noise is the most consistent method to defend against adversarial attacks with a success rate of averaged across all models and attack algorithms.23924
Infra-red Pupil Detection for Use in a Face Recognition System
This paper presents a new method of eye localisation and face segmentation for use in a face recognition system. By using two near infrared light sources, we have shown that the face can be coarsely segmented, and the eyes can be accurately located, increasing the accuracy of the face localisation and improving the overall speed of the system. The system is able to locate both eyes within 25% of the eye-to-eye distance in over 96% of test cases
Anomalous behaviour in loss-gradient based interpretability methods
Loss-gradients are used to interpret the decision making process of deep
learning models. In this work, we evaluate loss-gradient based attribution
methods by occluding parts of the input and comparing the performance of the
occluded input to the original input. We observe that the occluded input has
better performance than the original across the test dataset under certain
conditions. Similar behaviour is observed in sound and image recognition tasks.
We explore different loss-gradient attribution methods, occlusion levels and
replacement values to explain the phenomenon of performance improvement under
occlusion.Comment: Accepted at ICLR RobustML workshop 202
Bader’s theory of atoms in molecules (AIM) and its applications to chemical bonding
In this perspective article, the basic theory and applications of the "Quantum Theory of Atoms
in Molecules" have been presented with examples from different categories of weak and hydrogen bonded
molecular systems
Curriculum based dropout discriminator for domain adaptation
Domain adaptation is essential to enable wide usage of deep learning based
networks trained using large labeled datasets. Adversarial learning based
techniques have shown their utility towards solving this problem using a
discriminator that ensures source and target distributions are close. However,
here we suggest that rather than using a point estimate, it would be useful if
a distribution based discriminator could be used to bridge this gap. This could
be achieved using multiple classifiers or using traditional ensemble methods.
In contrast, we suggest that a Monte Carlo dropout based ensemble discriminator
could suffice to obtain the distribution based discriminator. Specifically, we
propose a curriculum based dropout discriminator that gradually increases the
variance of the sample based distribution and the corresponding reverse
gradients are used to align the source and target feature representations. The
detailed results and thorough ablation analysis show that our model outperforms
state-of-the-art results.Comment: BMVC 2019 Accepted, Project Page:
https://delta-lab-iitk.github.io/CD3A
- …