22,757 research outputs found
CAGFuzz: Coverage-Guided Adversarial Generative Fuzzing Testing of Deep Learning Systems
Deep Learning systems (DL) based on Deep Neural Networks (DNNs) are more and
more used in various aspects of our life, including unmanned vehicles, speech
processing, and robotics. However, due to the limited dataset and the
dependence on manual labeling data, DNNs often fail to detect their erroneous
behaviors, which may lead to serious problems. Several approaches have been
proposed to enhance the input examples for testing DL systems. However, they
have the following limitations. First, they design and generate adversarial
examples from the perspective of model, which may cause low generalization
ability when they are applied to other models. Second, they only use surface
feature constraints to judge the difference between the adversarial example
generated and the original example. The deep feature constraints, which contain
high-level semantic information, such as image object category and scene
semantics are completely neglected. To address these two problems, in this
paper, we propose CAGFuzz, a Coverage-guided Adversarial Generative Fuzzing
testing approach, which generates adversarial examples for a targeted DNN to
discover its potential defects. First, we train an adversarial case generator
(AEG) from the perspective of general data set. Second, we extract the depth
features of the original and adversarial examples, and constrain the
adversarial examples by cosine similarity to ensure that the semantic
information of adversarial examples remains unchanged. Finally, we retrain
effective adversarial examples to improve neuron testing coverage rate. Based
on several popular data sets, we design a set of dedicated experiments to
evaluate CAGFuzz. The experimental results show that CAGFuzz can improve the
neuron coverage rate, detect hidden errors, and also improve the accuracy of
the target DNN
Weakly Supervised Object Discovery by Generative Adversarial & Ranking Networks
The deep generative adversarial networks (GAN) recently have been shown to be
promising for different computer vision applications, like image edit- ing,
synthesizing high resolution images, generating videos, etc. These networks and
the corresponding learning scheme can handle various visual space map- pings.
We approach GANs with a novel training method and learning objective, to
discover multiple object instances for three cases: 1) synthesizing a picture
of a specific object within a cluttered scene; 2) localizing different
categories in images for weakly supervised object detection; and 3) improving
object discov- ery in object detection pipelines. A crucial advantage of our
method is that it learns a new deep similarity metric, to distinguish multiple
objects in one im- age. We demonstrate that the network can act as an
encoder-decoder generating parts of an image which contain an object, or as a
modified deep CNN to rep- resent images for object detection in supervised and
weakly supervised scheme. Our ranking GAN offers a novel way to search through
images for object specific patterns. We have conducted experiments for
different scenarios and demonstrate the method performance for object
synthesizing and weakly supervised object detection and classification using
the MS-COCO and PASCAL VOC datasets
Cross-Entropy Adversarial View Adaptation for Person Re-identification
Person re-identification (re-ID) is a task of matching pedestrians under
disjoint camera views. To recognise paired snapshots, it has to cope with large
cross-view variations caused by the camera view shift. Supervised deep neural
networks are effective in producing a set of non-linear projections that can
transform cross-view images into a common feature space. However, they
typically impose a symmetric architecture, yielding the network ill-conditioned
on its optimisation. In this paper, we learn view-invariant subspace for person
re-ID, and its corresponding similarity metric using an adversarial view
adaptation approach. The main contribution is to learn coupled asymmetric
mappings regarding view characteristics which are adversarially trained to
address the view discrepancy by optimising the cross-entropy view confusion
objective. To determine the similarity value, the network is empowered with a
similarity discriminator to promote features that are highly discriminant in
distinguishing positive and negative pairs. The other contribution includes an
adaptive weighing on the most difficult samples to address the imbalance of
within/between-identity pairs. Our approach achieves notable improved
performance in comparison to state-of-the-arts on benchmark datasets.Comment: Appearing at IEEE Transactions on Circuits and Systems for Video
Technolog
Binary Generative Adversarial Networks for Image Retrieval
The most striking successes in image retrieval using deep hashing have mostly
involved discriminative models, which require labels. In this paper, we use
binary generative adversarial networks (BGAN) to embed images to binary codes
in an unsupervised way. By restricting the input noise variable of generative
adversarial networks (GAN) to be binary and conditioned on the features of each
input image, BGAN can simultaneously learn a binary representation per image,
and generate an image plausibly similar to the original one. In the proposed
framework, we address two main problems: 1) how to directly generate binary
codes without relaxation? 2) how to equip the binary representation with the
ability of accurate image retrieval? We resolve these problems by proposing new
sign-activation strategy and a loss function steering the learning process,
which consists of new models for adversarial loss, a content loss, and a
neighborhood structure loss. Experimental results on standard datasets
(CIFAR-10, NUSWIDE, and Flickr) demonstrate that our BGAN significantly
outperforms existing hashing methods by up to 107\% in terms of~mAP (See Table
tab.res.map.comp) Our anonymous code is available at:
https://github.com/htconquer/BGAN.Comment: arXiv admin note: text overlap with arXiv:1702.00758 by other author
Generative Adversarial Network in Medical Imaging: A Review
Generative adversarial networks have gained a lot of attention in the
computer vision community due to their capability of data generation without
explicitly modelling the probability density function. The adversarial loss
brought by the discriminator provides a clever way of incorporating unlabeled
samples into training and imposing higher order consistency. This has proven to
be useful in many cases, such as domain adaptation, data augmentation, and
image-to-image translation. These properties have attracted researchers in the
medical imaging community, and we have seen rapid adoption in many traditional
and novel applications, such as image reconstruction, segmentation, detection,
classification, and cross-modality synthesis. Based on our observations, this
trend will continue and we therefore conducted a review of recent advances in
medical imaging using the adversarial training scheme with the hope of
benefiting researchers interested in this technique.Comment: 24 pages; v4; added missing references from before Jan 1st 2019;
accepted to MedI
Attacks on State-of-the-Art Face Recognition using Attentional Adversarial Attack Generative Network
With the broad use of face recognition, its weakness gradually emerges that
it is able to be attacked. So, it is important to study how face recognition
networks are subject to attacks. In this paper, we focus on a novel way to do
attacks against face recognition network that misleads the network to identify
someone as the target person not misclassify inconspicuously. Simultaneously,
for this purpose, we introduce a specific attentional adversarial attack
generative network to generate fake face images. For capturing the semantic
information of the target person, this work adds a conditional variational
autoencoder and attention modules to learn the instance-level correspondences
between faces. Unlike traditional two-player GAN, this work introduces face
recognition networks as the third player to participate in the competition
between generator and discriminator which allows the attacker to impersonate
the target person better. The generated faces which are hard to arouse the
notice of onlookers can evade recognition by state-of-the-art networks and most
of them are recognized as the target person
Context-Aware Semantic Inpainting
Recently image inpainting has witnessed rapid progress due to generative
adversarial networks (GAN) that are able to synthesize realistic contents.
However, most existing GAN-based methods for semantic inpainting apply an
auto-encoder architecture with a fully connected layer, which cannot accurately
maintain spatial information. In addition, the discriminator in existing GANs
struggle to understand high-level semantics within the image context and yield
semantically consistent content. Existing evaluation criteria are biased
towards blurry results and cannot well characterize edge preservation and
visual authenticity in the inpainting results. In this paper, we propose an
improved generative adversarial network to overcome the aforementioned
limitations. Our proposed GAN-based framework consists of a fully convolutional
design for the generator which helps to better preserve spatial structures and
a joint loss function with a revised perceptual loss to capture high-level
semantics in the context. Furthermore, we also introduce two novel measures to
better assess the quality of image inpainting results. Experimental results
demonstrate that our method outperforms the state of the art under a wide range
of criteria
HashGAN:Attention-aware Deep Adversarial Hashing for Cross Modal Retrieval
As the rapid growth of multi-modal data, hashing methods for cross-modal
retrieval have received considerable attention. Deep-networks-based cross-modal
hashing methods are appealing as they can integrate feature learning and hash
coding into end-to-end trainable frameworks. However, it is still challenging
to find content similarities between different modalities of data due to the
heterogeneity gap. To further address this problem, we propose an adversarial
hashing network with attention mechanism to enhance the measurement of content
similarities by selectively focusing on informative parts of multi-modal data.
The proposed new adversarial network, HashGAN, consists of three building
blocks: 1) the feature learning module to obtain feature representations, 2)
the generative attention module to generate an attention mask, which is used to
obtain the attended (foreground) and the unattended (background) feature
representations, 3) the discriminative hash coding module to learn hash
functions that preserve the similarities between different modalities. In our
framework, the generative module and the discriminative module are trained in
an adversarial way: the generator is learned to make the discriminator cannot
preserve the similarities of multi-modal data w.r.t. the background feature
representations, while the discriminator aims to preserve the similarities of
multi-modal data w.r.t. both the foreground and the background feature
representations. Extensive evaluations on several benchmark datasets
demonstrate that the proposed HashGAN brings substantial improvements over
other state-of-the-art cross-modal hashing methods.Comment: 10 pages, 8 figures, 3 table
Generating Images with Perceptual Similarity Metrics based on Deep Networks
Image-generating machine learning models are typically trained with loss
functions based on distance in the image space. This often leads to
over-smoothed results. We propose a class of loss functions, which we call deep
perceptual similarity metrics (DeePSiM), that mitigate this problem. Instead of
computing distances in the image space, we compute distances between image
features extracted by deep neural networks. This metric better reflects
perceptually similarity of images and thus leads to better results. We show
three applications: autoencoder training, a modification of a variational
autoencoder, and inversion of deep convolutional networks. In all cases, the
generated images look sharp and resemble natural images.Comment: minor correction
A Study of Cross-domain Generative Models applied to Cartoon Series
We investigate Generative Adversarial Networks (GANs) to model one particular
kind of image: frames from TV cartoons. Cartoons are particularly interesting
because their visual appearance emphasizes the important semantic information
about a scene while abstracting out the less important details, but each
cartoon series has a distinctive artistic style that performs this abstraction
in different ways. We consider a dataset consisting of images from two popular
television cartoon series, Family Guy and The Simpsons. We examine the ability
of GANs to generate images from each of these two domains, when trained
independently as well as on both domains jointly. We find that generative
models may be capable of finding semantic-level correspondences between these
two image domains despite the unsupervised setting, even when the training data
does not give labeled alignments between them
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