论文标题
关于灰度对抗图像领域中FNN和CNN性能偏差的研究
A study on the deviations in performance of FNNs and CNNs in the realm of grayscale adversarial images
论文作者
论文摘要
神经网络很容易在噪声扰动的图像分类中具有较低的精度。 CNN卷积神经网络以其在良性图像的分类中无与伦比的精度而闻名。但是我们的研究表明，它们极易受到噪声的攻击，而馈送前向神经网络，FNN与噪声扰动的对应性较小，几乎不受干扰地保持其准确性。观察到FNN可以更好地分类噪声密集的单向图像，而这些图像只是人类视力的巨大噪音。在我们的研究中，我们使用了手写数字数据集，MNIST与以下架构：具有1和2个隐藏层和CNN的FNN，带有3、4、6和8卷积，并分析了其准确性。 FNN脱颖而出表明，无论噪声强度如何，它们的分类精度超过85％。在我们通过此数据对CNN的分析中，使用8个卷积的CNN的分类准确性减速是其余CNN的一半。准确性趋势的相关分析和数学建模是这些结论的路线图。
Neural Networks are prone to having lesser accuracy in the classification of images with noise perturbation. Convolutional Neural Networks, CNNs are known for their unparalleled accuracy in the classification of benign images. But our study shows that they are extremely vulnerable to noise addition while Feed-forward Neural Networks, FNNs show very less correspondence with noise perturbation, maintaining their accuracy almost undisturbed. FNNs are observed to be better at classifying noise-intensive, single-channeled images that are just sheer noise to human vision. In our study, we have used the hand-written digits dataset, MNIST with the following architectures: FNNs with 1 and 2 hidden layers and CNNs with 3, 4, 6 and 8 convolutions and analyzed their accuracies. FNNs stand out to show that irrespective of the intensity of noise, they have a classification accuracy of more than 85%. In our analysis of CNNs with this data, the deceleration of classification accuracy of CNN with 8 convolutions was half of that of the rest of the CNNs. Correlation analysis and mathematical modelling of the accuracy trends act as roadmaps to these conclusions.
