41,143 research outputs found
Adversarial Black-Box Attacks on Automatic Speech Recognition Systems using Multi-Objective Evolutionary Optimization
Fooling deep neural networks with adversarial input have exposed a
significant vulnerability in the current state-of-the-art systems in multiple
domains. Both black-box and white-box approaches have been used to either
replicate the model itself or to craft examples which cause the model to fail.
In this work, we propose a framework which uses multi-objective evolutionary
optimization to perform both targeted and un-targeted black-box attacks on
Automatic Speech Recognition (ASR) systems. We apply this framework on two ASR
systems: Deepspeech and Kaldi-ASR, which increases the Word Error Rates (WER)
of these systems by upto 980%, indicating the potency of our approach. During
both un-targeted and targeted attacks, the adversarial samples maintain a high
acoustic similarity of 0.98 and 0.97 with the original audio.Comment: Published in Interspeech 201
NAG: Network for Adversary Generation
Adversarial perturbations can pose a serious threat for deploying machine
learning systems. Recent works have shown existence of image-agnostic
perturbations that can fool classifiers over most natural images. Existing
methods present optimization approaches that solve for a fooling objective with
an imperceptibility constraint to craft the perturbations. However, for a given
classifier, they generate one perturbation at a time, which is a single
instance from the manifold of adversarial perturbations. Also, in order to
build robust models, it is essential to explore the manifold of adversarial
perturbations. In this paper, we propose for the first time, a generative
approach to model the distribution of adversarial perturbations. The
architecture of the proposed model is inspired from that of GANs and is trained
using fooling and diversity objectives. Our trained generator network attempts
to capture the distribution of adversarial perturbations for a given classifier
and readily generates a wide variety of such perturbations. Our experimental
evaluation demonstrates that perturbations crafted by our model (i) achieve
state-of-the-art fooling rates, (ii) exhibit wide variety and (iii) deliver
excellent cross model generalizability. Our work can be deemed as an important
step in the process of inferring about the complex manifolds of adversarial
perturbations.Comment: CVPR 201
Adversarial Discriminative Domain Adaptation
Adversarial learning methods are a promising approach to training robust deep
networks, and can generate complex samples across diverse domains. They also
can improve recognition despite the presence of domain shift or dataset bias:
several adversarial approaches to unsupervised domain adaptation have recently
been introduced, which reduce the difference between the training and test
domain distributions and thus improve generalization performance. Prior
generative approaches show compelling visualizations, but are not optimal on
discriminative tasks and can be limited to smaller shifts. Prior discriminative
approaches could handle larger domain shifts, but imposed tied weights on the
model and did not exploit a GAN-based loss. We first outline a novel
generalized framework for adversarial adaptation, which subsumes recent
state-of-the-art approaches as special cases, and we use this generalized view
to better relate the prior approaches. We propose a previously unexplored
instance of our general framework which combines discriminative modeling,
untied weight sharing, and a GAN loss, which we call Adversarial Discriminative
Domain Adaptation (ADDA). We show that ADDA is more effective yet considerably
simpler than competing domain-adversarial methods, and demonstrate the promise
of our approach by exceeding state-of-the-art unsupervised adaptation results
on standard cross-domain digit classification tasks and a new more difficult
cross-modality object classification task
KBGAN: Adversarial Learning for Knowledge Graph Embeddings
We introduce KBGAN, an adversarial learning framework to improve the
performances of a wide range of existing knowledge graph embedding models.
Because knowledge graphs typically only contain positive facts, sampling useful
negative training examples is a non-trivial task. Replacing the head or tail
entity of a fact with a uniformly randomly selected entity is a conventional
method for generating negative facts, but the majority of the generated
negative facts can be easily discriminated from positive facts, and will
contribute little towards the training. Inspired by generative adversarial
networks (GANs), we use one knowledge graph embedding model as a negative
sample generator to assist the training of our desired model, which acts as the
discriminator in GANs. This framework is independent of the concrete form of
generator and discriminator, and therefore can utilize a wide variety of
knowledge graph embedding models as its building blocks. In experiments, we
adversarially train two translation-based models, TransE and TransD, each with
assistance from one of the two probability-based models, DistMult and ComplEx.
We evaluate the performances of KBGAN on the link prediction task, using three
knowledge base completion datasets: FB15k-237, WN18 and WN18RR. Experimental
results show that adversarial training substantially improves the performances
of target embedding models under various settings.Comment: To appear at NAACL HLT 201
Understanding collaborative supply chain relationships through the application of the Williamson organisational failure framework
Many researchers have studied supply chain relationships however, the
preponderance of open markets situations and ‘industry-style’ surveys have
reduced the empirical focus on the dynamics of long-term, collaborative dyadic
relationships. Within the supply chain the need for much closer, long-term
relationships is increasing due to supplier rationalisation and globalisation
(Spekman et al, 1998) and more information about these interactions is required.
The research specifically tested the well-accepted Williamson’s (1975) Economic
Organisations Failure Framework as a theoretical model through which long term
collaborative relationships can be
- …