论文标题
使用合奏颜色空间模型来解决对抗性示例
Using an ensemble color space model to tackle adversarial examples
论文作者
论文摘要
图像中的分钟像素会大大改变深度学习模型的预测。例如，由于这种情况，可能出现的最重要的问题之一是自主驾驶。已经提出了许多方法，以不同的成功来对抗这一目标。我们提出了一种捍卫此类攻击的三步方法。首先，我们使用统计方法来确定图像。其次，我们表明，在同一模型中采用多种颜色空间可以帮助我们进一步对抗这些对抗性攻击，因为每个颜色空间都会发现某些特征明确。最后，将生成的特征地图放大并作为输入发送回去，以获得更小的功能。我们表明，所提出的模型不需要训练以捍卫特定类型的攻击，并且对黑盒，白色框和灰色盒子对抗攻击技术的本质更为强大。特别是，如果模型不接受对抗性示例训练，则该模型比比较模型的模型比比较模型高56.12％。
Minute pixel changes in an image drastically change the prediction that the deep learning model makes. One of the most significant problems that could arise due to this, for instance, is autonomous driving. Many methods have been proposed to combat this with varying amounts of success. We propose a 3 step method for defending such attacks. First, we denoise the image using statistical methods. Second, we show that adopting multiple color spaces in the same model can help us to fight these adversarial attacks further as each color space detects certain features explicit to itself. Finally, the feature maps generated are enlarged and sent back as an input to obtain even smaller features. We show that the proposed model does not need to be trained to defend an particular type of attack and is inherently more robust to black-box, white-box, and grey-box adversarial attack techniques. In particular, the model is 56.12 percent more robust than compared models in case of white box attacks when the models are not subject to adversarial example training.
