12,100 research outputs found
Domain Adaptive Transfer Attack (DATA)-based Segmentation Networks for Building Extraction from Aerial Images
Semantic segmentation models based on convolutional neural networks (CNNs)
have gained much attention in relation to remote sensing and have achieved
remarkable performance for the extraction of buildings from high-resolution
aerial images. However, the issue of limited generalization for unseen images
remains. When there is a domain gap between the training and test datasets,
CNN-based segmentation models trained by a training dataset fail to segment
buildings for the test dataset. In this paper, we propose segmentation networks
based on a domain adaptive transfer attack (DATA) scheme for building
extraction from aerial images. The proposed system combines the domain transfer
and adversarial attack concepts. Based on the DATA scheme, the distribution of
the input images can be shifted to that of the target images while turning
images into adversarial examples against a target network. Defending
adversarial examples adapted to the target domain can overcome the performance
degradation due to the domain gap and increase the robustness of the
segmentation model. Cross-dataset experiments and the ablation study are
conducted for the three different datasets: the Inria aerial image labeling
dataset, the Massachusetts building dataset, and the WHU East Asia dataset.
Compared to the performance of the segmentation network without the DATA
scheme, the proposed method shows improvements in the overall IoU. Moreover, it
is verified that the proposed method outperforms even when compared to feature
adaptation (FA) and output space adaptation (OSA).Comment: 11pages, 12 figure
Generative Adversarial Perturbations
In this paper, we propose novel generative models for creating adversarial
examples, slightly perturbed images resembling natural images but maliciously
crafted to fool pre-trained models. We present trainable deep neural networks
for transforming images to adversarial perturbations. Our proposed models can
produce image-agnostic and image-dependent perturbations for both targeted and
non-targeted attacks. We also demonstrate that similar architectures can
achieve impressive results in fooling classification and semantic segmentation
models, obviating the need for hand-crafting attack methods for each task.
Using extensive experiments on challenging high-resolution datasets such as
ImageNet and Cityscapes, we show that our perturbations achieve high fooling
rates with small perturbation norms. Moreover, our attacks are considerably
faster than current iterative methods at inference time.Comment: CVPR 2018, camera-ready versio
Detecting the Unexpected via Image Resynthesis
Classical semantic segmentation methods, including the recent deep learning
ones, assume that all classes observed at test time have been seen during
training. In this paper, we tackle the more realistic scenario where unexpected
objects of unknown classes can appear at test time. The main trends in this
area either leverage the notion of prediction uncertainty to flag the regions
with low confidence as unknown, or rely on autoencoders and highlight
poorly-decoded regions. Having observed that, in both cases, the detected
regions typically do not correspond to unexpected objects, in this paper, we
introduce a drastically different strategy: It relies on the intuition that the
network will produce spurious labels in regions depicting unexpected objects.
Therefore, resynthesizing the image from the resulting semantic map will yield
significant appearance differences with respect to the input image. In other
words, we translate the problem of detecting unknown classes to one of
identifying poorly-resynthesized image regions. We show that this outperforms
both uncertainty- and autoencoder-based methods
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