5,909 research outputs found
Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning
Visual language grounding is widely studied in modern neural image captioning
systems, which typically adopts an encoder-decoder framework consisting of two
principal components: a convolutional neural network (CNN) for image feature
extraction and a recurrent neural network (RNN) for language caption
generation. To study the robustness of language grounding to adversarial
perturbations in machine vision and perception, we propose Show-and-Fool, a
novel algorithm for crafting adversarial examples in neural image captioning.
The proposed algorithm provides two evaluation approaches, which check whether
neural image captioning systems can be mislead to output some randomly chosen
captions or keywords. Our extensive experiments show that our algorithm can
successfully craft visually-similar adversarial examples with randomly targeted
captions or keywords, and the adversarial examples can be made highly
transferable to other image captioning systems. Consequently, our approach
leads to new robustness implications of neural image captioning and novel
insights in visual language grounding.Comment: Accepted by 56th Annual Meeting of the Association for Computational
Linguistics (ACL 2018). Hongge Chen and Huan Zhang contribute equally to this
wor
MeshAdv: Adversarial Meshes for Visual Recognition
Highly expressive models such as deep neural networks (DNNs) have been widely
applied to various applications. However, recent studies show that DNNs are
vulnerable to adversarial examples, which are carefully crafted inputs aiming
to mislead the predictions. Currently, the majority of these studies have
focused on perturbation added to image pixels, while such manipulation is not
physically realistic. Some works have tried to overcome this limitation by
attaching printable 2D patches or painting patterns onto surfaces, but can be
potentially defended because 3D shape features are intact. In this paper, we
propose meshAdv to generate "adversarial 3D meshes" from objects that have rich
shape features but minimal textural variation. To manipulate the shape or
texture of the objects, we make use of a differentiable renderer to compute
accurate shading on the shape and propagate the gradient. Extensive experiments
show that the generated 3D meshes are effective in attacking both classifiers
and object detectors. We evaluate the attack under different viewpoints. In
addition, we design a pipeline to perform black-box attack on a photorealistic
renderer with unknown rendering parameters.Comment: Published in IEEE CVPR201
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