Generating adversarial examples is an intriguing problem and an important way of understanding the working mechanism of deep neural networks. Recently, it has attracted a lot of attention in the computer vision community. Most existing approaches generated perturbations in image space, i.e., each pixel can be modified independently. However, it remains unclear whether these adversarial examples are authentic, in the sense that they correspond to actual changes in physical properties. This paper aims at exploring this topic in the contexts of object classification and visual question answering. The baselines are set to be several state-of-the-art deep neural networks which receive 2D input images. We augment these networks with a differentiable 3D rendering layer in front, so that a 3D scene (in physical space) is rendered into a 2D image (in image space), and then mapped to a prediction (in output space). There are two (direct or indirect) ways of attacking the physical parameters. The former back-propagates the gradients of error signals from output space to physical space directly, while the latter first constructs an adversary in image space, and then attempts to find the best solution in physical space that is rendered into this image. An important finding is that attacking physical space is much more difficult, as the direct method, compared with that used in image space, produces a much lower success rate and requires heavier perturbations to be added. On the other hand, the indirect method does not work out, suggesting that adversaries generated in image space are inauthentic. By interpreting them in physical space, most of these adversaries can be filtered out, showing promise in defending adversaries.
Deep learning is the state-of-the-art machine learning technique to learn visual representations from labeled data. Yet despite the success of deep learning, it remains challenging to explain what is learned by these complicated models. One of the most interesting efforts towards this goal is to generated adversaries. In terms of adversaries @cite_38 , we are talking about small noise that is (i) imperceptible to humans, and (ii) able to cause deep neural networks make wrong predictions after being added to the input image. It was shown that such perturbations can be easily generated in a white-box environment, i.e. , both the network structure and pre-trained weights are known to the attacker. Early studies were mainly focused on image classification @cite_5 @cite_29 . But soon, researchers were able to attack deep networks for detection and segmentation @cite_17 , and also visual question answering @cite_12 . Efforts were also made in finding universal perturbations which can transfer across images @cite_13 , as well as adversarial examples in the physical world which were produced by taking photos on the printed perturbed images @cite_11 .
@cite_38 @cite_11 @cite_29 @cite_5 @cite_13 @cite_12 @cite_17
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While deep learning is remarkably successful on perceptual tasks, it was also shown to be vulnerable to adversarial perturbations of the input. These perturbations denote noise added to the input that was generated specifically to fool the system while being quasi-imperceptible for humans. More severely, there even exist universal perturbations that are input-agnostic but fool the network on the majority of inputs. While recent work has focused on image classification, this work proposes attacks against semantic image segmentation: we present an approach for generating (universal) adversarial perturbations that make the network yield a desired target segmentation as output. We show empirically that there exist barely perceptible universal noise patterns which result in nearly the same predicted segmentation for arbitrary inputs. Furthermore, we also show the existence of universal noise which removes a target class (e.g., all pedestrians) from the segmentation while leaving the segmentation mostly unchanged otherwise. While deep learning is remarkably successful on perceptual tasks, it was also shown to be vulnerable to adversarial perturbations of the input. These perturbations denote noise added to the input that was generated specifically to fool the system while being quasi-imperceptible for humans. More severely, there even exist universal perturbations that are input-agnostic but fool the network on the majority of inputs. While recent work has focused on image classification, this work proposes attacks against semantic image segmentation: we present an approach for generating (universal) adversarial perturbations that make the network yield a desired target segmentation as output. We show empirically that there exist barely perceptible universal noise patterns which result in nearly the same predicted segmentation for arbitrary inputs. Furthermore, we also show the existence of universal noise which removes a target class (e.g., all pedestrians) from the segmentation while leaving the segmentation mostly unchanged otherwise. Deep neural networks are known to be vulnerable to adversarial examples, i.e., images that are maliciously perturbed to fool the model. Generating adversarial examples has been mostly limited to finding small perturbations that maximize the model prediction error. Such images, however, contain artificial perturbations that make them somewhat distinguishable from natural images. This property is used by several defense methods to counter adversarial examples by applying denoising filters or training the model to be robust to small perturbations. In this paper, we introduce a new class of adversarial examples, namely "Semantic Adversarial Examples," as images that are arbitrarily perturbed to fool the model, but in such a way that the modified image semantically represents the same object as the original image. We formulate the problem of generating such images as a constrained optimization problem and develop an adversarial transformation based on the shape bias property of human cognitive system. In our method, we generate adversarial images by first converting the RGB image into the HSV (Hue, Saturation and Value) color space and then randomly shifting the Hue and Saturation components, while keeping the Value component the same. Our experimental results on CIFAR10 dataset show that the accuracy of VGG16 network on adversarial color-shifted images is 5.7 . Deep neural networks are known to be vulnerable to adversarial examples, i.e., images that are maliciously perturbed to fool the model. Generating adversarial examples has been mostly limited to finding small perturbations that maximize the model prediction error. Such images, however, contain artificial perturbations that make them somewhat distinguishable from natural images. This property is used by several defense methods to counter adversarial examples by applying denoising filters or training the model to be robust to small perturbations. In this paper, we introduce a new class of adversarial examples, namely "Semantic Adversarial Examples," as images that are arbitrarily perturbed to fool the model, but in such a way that the modified image semantically represents the same object as the original image. We formulate the problem of generating such images as a constrained optimization problem and develop an adversarial transformation based on the shape bias property of human cognitive system. In our method, we generate adversarial images by first converting the RGB image into the HSV (Hue, Saturation and Value) color space and then randomly shifting the Hue and Saturation components, while keeping the Value component the same. Our experimental results on CIFAR10 dataset show that the accuracy of VGG16 network on adversarial color-shifted images is 5.7 .