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Title: Anomalous Pattern Detection in Activations and Reconstruction Error of Autoencoders

Paper content:
In real-world machine learning applications, large outliers and pervasive noise are commonplace, and access to clean training data as required by standard deep autoencoders is unlikely.
Reliably detecting anomalies in a given set of images is a task of high practical relevance for visual quality inspection, surveillance, or medical image analysis.
Autoencoder neural networks learn to reconstruct normal images, and hence can classify those images as anomalous if the reconstruction error exceeds some threshold.
In this paper, we proposed an unsupervised method based on subset scanning over autoencoder activations.
The contributions of our work are threefold.
First, we propose a novel method combining detection with reconstruction error and subset scanning scores to improve the anomaly score of current autoencoders without requiring any retraining.
Second, we provide the ability to inspect and visualize the set of anomalous nodes in the reconstruction error space that make a sample noised.
Third, we show that subset scanning can be used for anomaly detection in the inner layers of the autoencoder.
We provide detection power results for several untargeted adversarial noise models under standard datasets.
Neural networks generate a large amount of activation data when processing an input.
This work applies anomalous pattern detection techniques on this activation data in order to determine if the input is anomalous.
Examples of an anomalous input can be noised samples by an adversary (Szegedy et al., 2013; Goodfellow et al., 2014; Kurakin et al., 2016a; Dalvi et al., 2004a) , human annotation errors (Klebanov et al., 2008) , etc.
The goal of anomalous pattern detection is to quantify, detect, and characterize the data that are generated by an alternative process.
Since anomalies are rare and come from diverse sources, it is not feasible to obtain labeled datasets of all possible anomalies/attacks.
If an observation deviates from the learned model, it is classified as an anomaly (Chandola et al., 2009) .
In real-world problems, large outliers and pervasive perturbations are commonplace, and one may not have access to clean training data as required by standard deep denoising autoencoders (Beggel et al., 2019) due to reasons such as human annotation errors (Klebanov et al., 2008) and poisoning techniques (Dalvi et al., 2004b) .
Autoencoders differ from classical classifier networks such as Convolutional Neural Networks (CNNs) .
Autoencoders do not require labels because the expected output is the input data.
The autoencoder is trained to minimize the reconstruction error L(x, x ).
During the prediction step, anomaly detection can be performed by looking at the distribution of mean reconstruction error L(w, d(e(w))) when w ∈ X clean and L(w , d(e(w ))) when w ∈ X adv (Frosst et al., 2018 ).
An example of both, clean and noise reconstruction error distribution can be seen in Figure 4 (b).
Using this type of anomaly detection with autoencoders assumes that the autoencoder is properly trained with clean data.
Otherwise, this manifold can be used advantageously by training the autoencoder with corrupted samples that are mapped to clean samples.
As a result, the autoencoder will learn an underlying vector field that points in the direction of the manifold in which the clean samples lie.
Thus, upon the introduction of a perturbation, the magnitude of each arrow in the vector field will indicate the direction in which the data must be moved to map the sample to its clean representation (Sahay et al., 2019) .
Further detail on the autoencoder architecture and training setup for the experiments can be found in the Section A.4.
Subset scanning frames the detection problem as a search over subsets of data in order to find a subset that maximizes a scoring function F (S), typically a likelihood ratio.
Subset scanning exploits a property of these scoring functions that allow for efficient maximization over the exponentially large search space (Neill, 2012) .
In this paper, we show how subset scanning methods can enhance the anomaly detection power of autoencoders in an unsupervised manner and without a retraining step.
We treat this anomaly detection approach as a search for a subset of node activations that are higher than expected.
This is formally quantified as the subset with the highest score according to a non-parametric scan statistic.
The contributions of our work are threefold.
First, we propose a novel approach combining detection with reconstruction error and subset scanning scores to improve the anomaly score of current autoencoders without requiring any retraining.
Second, we provide the ability to identify and visualize the set of anomalous nodes in the reconstruction error space that make noised samples.
Third, we show that subset scanning can be used for anomaly detection in the inner layers of the autoencoder.
Figure 1: Example of subset scanning score distributions across layers of an autoencoder for adversarial BIM noise = 0.01.
In the top of the graph we can see subset score distributions per nodes in a layer.
The distributions of subset scanning scores are shown in blue for clean images (C) (expected distribution), and in orange for noised samples A t .
Higher AUCs are expected when distributions are separated from each other and lower AUCs when they overlap.
The purple structure corresponds to convolutional layers at the Encoder, while the red structure corresponds to the convolution layers for the Decoder.
The computed AUC for the subset score distributions can be found in Table 1 .
The highest mutual information exchange with the adversarial input happens on the first layers (convolutional and maxpooling).
This is why the greatest divergence in both C and A t subset scores distributions is seen.
In the latent space, due to properties described in Section 4, the autoencoder abstracts basic representations of the images, losing subset scanning power due to the autoencoder mapping the new sample to the expected distribution.
This can be seen as an almost perfect overlap of distribution in conv 2d 7.
In this work, we proposed a novel unsupervised method for adversarial noise detection with off-theshelf autoencoders and subset scanning.
We have successfully demonstrated how subset scanning can be used to gain detection strength against multiple adversarial attacks on images across several datasets, without requiring any retraining or complex deep autoencoder network structures.
Furthermore, we tested subset scanning over the reconstruction error space and observed significant variations depending on the dataset, autoencoder architecture, and training setup.
We performed Figure 5 : Anomalous nodes visualization.
Overlap of anomalous nodes (white) and reconstruction error (darker blue) per sample.
(a) Noised samples with BIM.
We can observe that nodes outside the contour will make the sample be classified as noised.
(b) Whereas clean we expect the anomalous nodes will be along the contour of the figure.
preliminary experiments that yielded a relation between a decrease in the loss of the trained autoencoder and an increase in the detection power of subset scanning under the reconstruction error space.
Nonetheless, applying our method under this space provides introspection capabilities that allow us to identify the nodes or portions of the input image look anomalous.
Consequently, we are able to not only point out which image looks anomalous but also characterize the nodes that make the input a noised sample.
We also evaluated the performance of applying subset scanning over the autoencoder's activations.
We observed a consistent and high detection power results across noise attacks, datasets, autoencoders architectures and different noised training levels in the initial layers (Convolutional and MaxPooling layers).
Due to versatile properties of subset scanning under neural network activation analysis it may be used for several other studies, including unsupervised classification in the latent space of an autoencoder.
We would expect that same class images will identify as a subset of inputs (images) that have higher-than-expected activations (i.e. large number of low empirical p−values) at a subset of nodes.
Subset scanning applied to autoencoders activations is a novel, unsupervised anomaly detector that can be applied to any pre-trained, off-the-shelf neural network, previously only used in classifier neural networks such as CNNs and ResNet (Speakman et al., 2018) .

assistant: text: Unsupervised method to detect adversarial samples in autoencoder's activations and reconstruction error space