Convolutional Neural Networks on Randomized Data

Convolutional Neural Networks on Randomized Data

Cristian Ivan
Romanian Institute of Science and Technology
Cireşilor 29, 400487 Cluj-Napoca, Romania

Convolutional Neural Networks (CNNs) are build specifically for computer vision tasks for which it is known that the input data is a hierarchical structure based on locally correlated elements. The question that naturally arises is what happens with the performance of CNNs if one of the basic properties of the data is removed, e.g. what happens if the image pixels are randomly permuted? Intuitively one expects that the convolutional network performs poorly in these circumstances in contrast to a multilayer perceptron (MLPs) whose classification accuracy should not be affected by the pixel randomization. This work shows that by randomizing image pixels the hierarchical structure of the data is destroyed and long range correlations are introduced which standard CNNs are not able to capture. We show that their classification accuracy is heavily dependent on the class similarities as well as the pixel randomization process. We also indicate that dilated convolutions are able to recover some of the pixel correlations and improve the performance.

1 Introduction

Convolutional Neural Networks are inspired by the visual system of living organisms and are built to exploit the 2D structure of natural images [12]. The receptive field offered by the convolution kernels greatly reduces the number of trainable parameters and increases the performance of these networks as compared to fully connected feed forward networks. CNNs are used not only for visual tasks but also on other kind of data where local correlations are still present.

One question that can be asked is what is the performance of CNNs when trained on images where the individual pixels are randomly permuted? Since the local spatial structure of an image is destroyed one would expect that a convolutional network is not able to find representative features of the data and the accuracy should be very low.

For this study two types of feed-forward networks are used for image classification tasks. CNNs and MLPs are trained on natural images and their pixel-wise permutations. The hyper-parameters of the networks are kept the same throughout the performed experiments and are trained for the same number of epochs. The MLP is used as a baseline and sanity check for the analysis and, due to the known limitations it has when trained on complex image databases, its performance is not intended to be used as a direct comparison to the CNN performance.

The chosen architecture of the CNN follows closely the VGG16 network [10] and the MLP consists of the last fully connected layers of the CNN. The only difference between the CNN used in this paper and the VGGNet is the total number of parameters since they are trained on much smaller images, 28 and 32 pixels per side. Both networks in this study use adam optimizer [5] with a learning rate of 0.0001 and a decay of . The training procedure does not use any form of data augmentation.

We perform three types of experiments where we compare the classification accuracy of a CNN trained on natural images with the accuracy of a CNN with the same architecture trained on images whose contents are randomized based on three different procedures.

In the first experiment the order of the image pixels is fully shuffled. The classification accuracy of the CNN is investigated for increasingly complex datasets: MNIST [7], Fashion-MNIST [11] and CIFAR10 [6]. In the second and third experiment we develop two different parametrized methods for controlling the image randomization and investigate the CNN classification accuracy on only the CIFAR10 dataset.

2 Pixel-wise permutations

If we consider the pixels of an image in row-major order as the set then a pixel-wise permutation can be expressed in Cauchy’s two-line notation as:


where the second line represents the new arrangement of the original pixels in the permuted image. Therefore pixel 1 from the original image is moved to position 4 in the permuted image, pixel 2 is moved to position 5 etc.

2.1 Mnist

A sample of both natural and pixel-wise permuted images is shown in Figure 1: the left panel shows handwritten digits from the MNIST dataset and the right panel shows a pixel permutation of those images. The same permutation is applied on all train and test images.

Figure 1: Left panel: random samples of MNIST natural images; right panel: pixel-wise randomization of the same sample

The test data accuracy of the networks can be seen in Figure 2. The CNN trained on natural images reaches an accuracy of while the one trained on permuted images shows a delayed learning curve as well as a consistently lower performance.

Figure 2: Accuracy of a CNN and MLP running on MNIST images and their permutations (color online)

The performance of the MLP trained on both natural and pixel-wise permuted images is almost identical throughout the entire training phase. A more interesting observation is that the MLP, which consists of only the last fully connected layers of the CNN, has higher performance on permuted images than the CNN at every point during the training phase.

2.2 Fashion-MNIST

The Fashion-MNIST dataset has a higher complexity than MNIST and poses a greater difficulty for the networks. The left panel of Figure 3 shows a sample of the greater variability of clothing images and their pixel-wise permutations.

Figure 3: Left panel: random sample of Fashion-MNIST images; right panel: pixel-wise randomization of the same sample

Figure 4 shows a lower performance, as compared to MNIST, on natural and pixel-wise randomized images for both types of networks. The peak performance of the CNN trained on the natural images is , decreasing to when trained on the randomized data set. The same behaviour is observed as in the previous experiment: the test accuracy of the CNN trained on permuted images is consistently below the MLP.

Figure 4: Accuracy of a CNN and MLP trained on Fashion-MNIST dataset.

2.3 Cifar10

The CIFAR10 dataset is far more complex than the previous two: the images are in 3 color channels, the objects are not centered, they are less similar to each other and the background is not uniform. Figure 5 shows the natural and pixel-wise randomization images, for the latter the identical randomization procedure being applied to all channels. The various labels are listed in table 3.

Training a CNN on this database reveals an even lower classification accuracy when using natural images, slightly below , and a dramatic performance decrease when training on pixel-wise permuted images, reaching only about , while the MLP is invariant under this type of transformation.

Figure 5: Left panel: random sample of CIFAR10 images; right panel: pixel-wise randomization of the same sample.

Figure 6 shows the evolution of the accuracy on the test images. There is again the trend of the accuracy of the CNN trained on permuted images to stay consistently below the baseline performance of the MLP. Table 1 summarizes the peak accuracies of the networks trained on all three datasets.

Figure 6: Accuracy of a CNN and MLP running on CIFAR10 images and their permutations.
Images natural permuted natural permuted
MNIST 99.5% 98.2% 98.7% 98.6%
Fashion 94.3% 89.6% 91.0% 90.9%
CIFAR10 88.9% 57.3% 59.3% 59.3%
Table 1: Accuracy of a CNN and MLP trained on natural images and their permutations.

3 Image patch permutations

To further investigate the behaviour of the network on data randomization a parametrized method is developed in order to gain a better control on the randomization process. The images are sliced in square patches which are then shuffled in the same manner as described by equation 2, where the numbers, instead of image pixels, denote the image patches. The parameter that controls the randomization is the size of the patch: a size of 1 is equivalent to a full pixel-wise permutation and a size of 32 is equivalent with the natural image. Figure 7 shows a few examples of permutations with intermediate patch sizes.

Figure 7: Example of a natural image (top-left) and its patch-wise randomization of size 16, 8 and 4.

Alternatively one can consider the randomization parameter the number of slices the image is cut into along an axis which results in a convenient series of powers of 2. The classification accuracy of the CNN trained with this parametrization is displayed in Figure 8. It shows how strongly the CNN performance is influenced by the size of the patches used for the randomization. Even when cutting the images in 4 slices per side the CNN looses in accuracy considerably.

Figure 8: CNN classification accuracy as a function of the number of patches used for randomization. 32 means the image was sliced in 32 patches of pixels, 16 means an image slices in 16 patches of pixels etc.

4 Local permutations

In this experiment the image pixels are randomized inside a restricted neighborhood. The algorithm is as follows: the image is scanned in a row-major order and each pixel is swapped with a randomly chosen pixel inside a neighborhood of pixels.

Figure 9: Examples of a natural image and its pixel-wise randomization of distances 1, 4 and 16.

A zero distance is equivalent to no permutation and a 32 distance to a full pixel-wise permutation. There are no restrictions on the number of times a pixel can be moved. Hence there is a non-zero chance that a pixel might migrate a distance longer than . This effect is rather small, as can be seen in Figure 9 where the top-right image shows a local permutation with distance 1.

Figure 10 shows the performance of the CNN as a function of several distances, ranging from 0 up to the size of the whole image. The larger the randomization distance is the less features the kernels are able to capture and the accuracy quickly decreases, reaching the same level as a CNN trained on fully pixel-wise randomized images.

Figure 10: CNN classification accuracy dependence on the randomization distance. The black curve correspond to a training on the RGB channels of CIFAR10 images and the red, green and blue curves show the accuracy for the corresponding color channel.

It is interesting to note how the CNN classification accuracy decreases when trained on separate image channels. Humans are able to recognize objects in images being either in color or gray-scale, with shape being the crucial factor in the classification process. The CNN correlates not only local pixels in terms of shape but also local pixels in terms of colors. Training on natural images and single channels results in a performance decrease as compared to training on all channels. When the local pixel correlations are destroyed the network performance degrades considerably more when trained on just single color channels. Comparing the color curves in Figure 10 with the black curve indicates that the network relies also on color correlations when doing classification, since the same randomization procedure is applied on all channels.

The color curves also show a slight data bias due to the growing discrepancy between the classification accuracy on the three different channels. Training on just the red channel results in the worst performance for all randomization distances indicating that there might be less information in this channel.

5 Discussion

Figures 2, 4 and 6 show common and consistent behaviors: the performance of the CNN drops when switching from training on natural to permuted images and always stays below the accuracy of the MLP throughout the whole training phase. The accuracy decrease cannot be attributed to a particular configuration of the network’s initial random weights because repeated trainings display the same effect.

In Figures 8 and 10 we have shown how the performance of the CNN changes as the image pixels are gradually randomized. The stronger the randomization is the lower the accuracy of the network becomes. By applying the same permutation to all examples the local patterns that repeat within the same image (e.g. edges at various inclinations) are destroyed but the intra-class similarities and inter-class differences are kept. The accuracy of the CNN decreases because the convolution kernels can not find hierarchical features in the randomized images but it does not decrease to the level of random guessing since the separation between classes still remains. It is unclear how to quantify the accuracy decrease the randomization induces.

The convolutional network has a built-in infinitely strong prior which constrains the values of some parameters to zero making it highly sensitive to the spatial structure of the data [3]. The more the local pixel correlation is removed the lower the classification accuracy becomes. The performance of a CNN has at least two independent components:

  1. like any other feed forward ANN, the network finds intra-class similarities and inter-class difference via gradient descent.

  2. the kernels learn local patterns occurring repeatedly within different regions of the same image, within different examples of the same class and within different examples of different classes.

Natural images are structures where local correlations are important. This is the reason why edges, corners, Gabor-like features etc. area learned by the CNN’s first few layers when trained on natural images [2]. Combining these basic building blocks in complex hierarchical structures results in large varieties of images and objects. At a more profound level it is speculated that this is the reason why deep learning works so well in practice and that the very structure of the universe is basically a large hierarchy of simple elements [8]. If pixels are moved in random positions across the image domain their initial local correlation is destroyed an it becomes a long range correlation. If there is a correlation among pixels from more distant locations a kernel spread on a wider area of the image is more likely to capture them than a local kernel.

Based on this idea we experimented with a modified version of the CNN where we replaced the whole stack of many convolution layers with two identical dilated convolutional layers: 64 filters with kernels, dilation rate of 4 and a stride of 1. Figure 11 shows a accuracy when training on pixel-wise permuted images indicating that some of the long range correlations are captured by the dilated kernels. There is a problematic aspect of this approach in that it does not permit arbitrary number of such layers due to the inevitable image size reduction of this convolution operation. We have also tested a single convolutional layer with an kernel with a stride of 4 but it did not exceed accuracy. Stacking many fully connected layers together did not surpass the classification accuracy of the dilated convolutional network indicating that deep MLPs are not a solution for this type of images.

Figure 11: Classification accuracy of the VGG16-like CNN, MLP and CNN with dilated convolutions trained on natural and permuted images.

6 Data properties

In this section we will present some basic properties of the data and show how strong the correlation between training on natural images vs. pixel-wise permutations is. This, in turn, indicates the underlying structure of the data even when individual image pixels are randomly permuted inside the images.

6.1 Fashion-MNIST

The Fashion-MNIST dataset is more complex than MNIST, as can be seen from the classification accuracy in Table 1. But the greater difficulty of this dataset comes not from the objects alone, but rather from the strong similarities between differently labeled classes. The left panel of Figure 3 shows how similar many examples of shirt look like T-shirt or coat. The average Fashion-MNIST images are shown in the top row of Figure 12 and the standard deviations in the bottom row.

Figure 12: Mean (upper row) and standard deviation (lower row) of Fashion-MNIST training images; the corresponding labels are listed in Table 2.
0 T-shirt/top 5 Sandal
1 Trouser 6 Shirt
2 Pullover 7 Sneaker
3 Dress 8 Bag
4 Coat 9 Ankle boot
Table 2: Fashion-MNIST labels

The confusion matrix in Figure 13 illustrates how often label 6 (shirt) is misclassified as T-shirt, coat, pullover and dress. If it were not for these strong similarities between labels 0, 2, 4 and 6 the overall performance of the CNN would be higher, most of the other categories having a classification accuracy significantly more than 90%. Other similar correlations can be seen in the non-zero entries in the column, which indicate that the network identified shoe-like features in sandal, sneaker and ankle boots.

Figure 13: Confusion matrix for CNN on natural Fashion-MNIST images.

6.2 Cifar10

CIFAR10 has considerably more variation than Fashion-MNIST and the mean and standard deviation figures are not relevant for this kind of analysis as the distributions are much more uniform. The image features and class commonalities are difficult to see by the naked eye and more sophisticated statistical tools are needed to shed light on the intra-class and inter-class correlations. However, by investigating the confusion matrix one can identify the correlations the networks learn.

Figure 14: Confusion matrix for CNN on natural CIFAR10 images (top panel) and pixel-wise randomizations (bottom).

Figure 14 shows the confusion matrices of the CNN trained on natural (top-panel) and pixel-wise permutations, respectively (bottom-panel). For training on natural images the top two highest accurate predictions are for the ship and automobile. The largest classification error is made in the case of the cat which is of the time misclassified as a dog. Notice that the confusion matrices are only approximately symmetric and the reverse misclassifications are slightly different, e.g. dog confused as a cat is about .

Figure 15: Confusion matrix for MLP on natural CIFAR10 images (top) and pixel-wise permutation (bottom).
0 airplane 5 dog
1 automobile 6 frog
2 bird 7 horse
3 cat 8 ship
4 deer 9 truck
Table 3: CIFAR10 labels

Although overall the classification accuracy drops from to when switching from training on natural images to permuted images the correlations made by the network in one case are very similar to the other case. One can observe the same central pattern in both figures. The top two classification accuracies are the same - ship and automobile and the cat-dog misclassification remains still the highest.

Figure 15 shows the confusion matrices of the MLP trained on natural and pixel-wise permuted images. Unlike the CNN, the overall accuracy difference is very small, , but some of the individual accuracies show a relatively high variation, the largest deviation being for bird and frog with and , respectively. A similarly strong cat-dog confusion is done also for this network.

We can obtain a quantification of the similarities between the two CNNs and MLPs by calculating the Pearson correlation coefficient between the prediction a network does for a particular category when trained on natural images and the prediction it makes for the same category when training on permuted images. In other words for the CNN we correlate the upper columns of Figure 14 with the lower columns from the same figure. The same calculations are performed for the MLP confusion matrices in Figure 15. Thus we obtain 10 correlation coefficients which are summarized in table 4 together with the average correlation coefficient.

airplane 0.951 0.862
automobile 0.974 0.995
bird 0.745 0.974
cat 0.963 0.960
deer 0.645 0.902
dog 0.982 0.983
frog 0.753 0.916
horse 0.907 0.926
ship 0.857 0.967
truck 0.925 0.992
Mean 0.870 0.947
Table 4: Correlations between the predictions of networks trained on natural images and pixel-wise permutations of CIFAR10 images.

7 Conclusions and further studies

This paper presents the limitations of convolutional neural networks when trained on images where individual pixel positions have been randomly permuted. We show a comparison of classification accuracies between CNNs trained on natural images and pixel-wise permutations together with the performance of an MLP as baseline. The absolute value of the accuracies are not relevant to the study, just the relative performances.

We have shown that the use of standard convolutional networks is inappropriate for cases where image pixels are permuted inside the image domain. We create long range correlations which can be better captured by kernels covering larger image areas than standard localized kernels. We have shown that by applying the same permutation to all images from the dataset there is still an underlying structure which can be discovered by neural networks. This suggests the possibility for further model improvements. It is important to design networks with architectures that can be invariant or at least less sensitive to data permutations or other types of data encryption. There are studies [4] which show that CNNs are still capable of classifying encrypted images, although in that particular case the transformation is a homomorphic encryption which preserves more of the data structure than the pixel randomizations do. This further raises the question whether it is possible to train a network on encrypted data but then be able to reconstruct the initial data once a few examples of human interpretable data become available.

Many types of analyses, where data is not necessary locally correlated, would benefit from such empowered architectures. For example, high energy physics experiments require the analysis of large data sets from particle collisions where the data appears on an event-by-event basis as random tracks in the detectors. However there are very strong underlying correlations since the subatomic processes obey the laws of physics. Often, [1] [9], CNNs are used for the analysis of features identified by physicists through standard methodologies. Networks which would perform well on seemingly random data would be of great use for this kind of studies.

Other domains could also greatly benefit from more powerful networks designed specifically for capturing long range correlations, situations which can easily arise in the experimental physical sciences.


  • [1] J. Duarte et al. Fast inference of deep neural networks in FPGAs for particle physics. JINST, 13(07):P07027, 2018.
  • [2] D. Erhan, Y. Bengio, A. Courville, and P. Vincent. Visualizing higher-layer features of a deep network. University of Montreal, 1341(3):1, 2009.
  • [3] I. Goodfellow, Y. Bengio, and A. Courville. Deep Learning. MIT Press, 2016.
  • [4] E. Hesamifard, H. Takabi, and M. Ghasemi. Cryptodl: Deep neural networks over encrypted data. CoRR, abs/1711.05189, 2017.
  • [5] D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. CoRR, abs/1412.6980, 2014.
  • [6] A. Krizhevsky. Learning multiple layers of features from tiny images. 2009.
  • [7] Y. LeCun and C. Cortes. MNIST handwritten digit database. 2010.
  • [8] H. W. Lin, M. Tegmark, and D. Rolnick. Why does deep and cheap learning work so well? Journal of Statistical Physics, 168(6):1223–1247, Sep 2017.
  • [9] C. F. Madrazo, I. H. Cacha, L. L. Iglesias, and J. M. de Lucas. Application of a convolutional neural network for image classification to the analysis of collisions in high energy physics. CoRR, abs/1708.07034, 2017.
  • [10] K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556, 2014.
  • [11] H. Xiao, K. Rasul, and R. Vollgraf. Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017.
  • [12] Y. B. Y. LeCun, L. Bottou and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, November 1998.
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