9 research outputs found
Adversarial Robustness through the Lens of Causality
The adversarial vulnerability of deep neural networks has attracted
significant attention in machine learning. From a causal viewpoint, adversarial
attacks can be considered as a specific type of distribution change on natural
data. As causal reasoning has an instinct for modeling distribution change, we
propose to incorporate causality into mitigating adversarial vulnerability.
However, causal formulations of the intuition of adversarial attack and the
development of robust DNNs are still lacking in the literature. To bridge this
gap, we construct a causal graph to model the generation process of adversarial
examples and define the adversarial distribution to formalize the intuition of
adversarial attacks. From a causal perspective, we find that the label is
spuriously correlated with the style (content-independent) information when an
instance is given. The spurious correlation implies that the adversarial
distribution is constructed via making the statistical conditional association
between style information and labels drastically different from that in natural
distribution. Thus, DNNs that fit the spurious correlation are vulnerable to
the adversarial distribution. Inspired by the observation, we propose the
adversarial distribution alignment method to eliminate the difference between
the natural distribution and the adversarial distribution. Extensive
experiments demonstrate the efficacy of the proposed method. Our method can be
seen as the first attempt to leverage causality for mitigating adversarial
vulnerability
Explicit Tradeoffs between Adversarial and Natural Distributional Robustness
Several existing works study either adversarial or natural distributional
robustness of deep neural networks separately. In practice, however, models
need to enjoy both types of robustness to ensure reliability. In this work, we
bridge this gap and show that in fact, explicit tradeoffs exist between
adversarial and natural distributional robustness. We first consider a simple
linear regression setting on Gaussian data with disjoint sets of core and
spurious features. In this setting, through theoretical and empirical analysis,
we show that (i) adversarial training with and norms
increases the model reliance on spurious features; (ii) For
adversarial training, spurious reliance only occurs when the scale of the
spurious features is larger than that of the core features; (iii) adversarial
training can have an unintended consequence in reducing distributional
robustness, specifically when spurious correlations are changed in the new test
domain. Next, we present extensive empirical evidence, using a test suite of
twenty adversarially trained models evaluated on five benchmark datasets
(ObjectNet, RIVAL10, Salient ImageNet-1M, ImageNet-9, Waterbirds), that
adversarially trained classifiers rely on backgrounds more than their
standardly trained counterparts, validating our theoretical results. We also
show that spurious correlations in training data (when preserved in the test
domain) can improve adversarial robustness, revealing that previous claims that
adversarial vulnerability is rooted in spurious correlations are incomplete.Comment: Accepted to NeurIPS 202
Deep Causal Learning: Representation, Discovery and Inference
Causal learning has attracted much attention in recent years because
causality reveals the essential relationship between things and indicates how
the world progresses. However, there are many problems and bottlenecks in
traditional causal learning methods, such as high-dimensional unstructured
variables, combinatorial optimization problems, unknown intervention,
unobserved confounders, selection bias and estimation bias. Deep causal
learning, that is, causal learning based on deep neural networks, brings new
insights for addressing these problems. While many deep learning-based causal
discovery and causal inference methods have been proposed, there is a lack of
reviews exploring the internal mechanism of deep learning to improve causal
learning. In this article, we comprehensively review how deep learning can
contribute to causal learning by addressing conventional challenges from three
aspects: representation, discovery, and inference. We point out that deep
causal learning is important for the theoretical extension and application
expansion of causal science and is also an indispensable part of general
artificial intelligence. We conclude the article with a summary of open issues
and potential directions for future work
A Review of the Role of Causality in Developing Trustworthy AI Systems
State-of-the-art AI models largely lack an understanding of the cause-effect
relationship that governs human understanding of the real world. Consequently,
these models do not generalize to unseen data, often produce unfair results,
and are difficult to interpret. This has led to efforts to improve the
trustworthiness aspects of AI models. Recently, causal modeling and inference
methods have emerged as powerful tools. This review aims to provide the reader
with an overview of causal methods that have been developed to improve the
trustworthiness of AI models. We hope that our contribution will motivate
future research on causality-based solutions for trustworthy AI.Comment: 55 pages, 8 figures. Under revie
Visual Processing and Latent Representations in Biological and Artificial Neural Networks
The human visual system performs the impressive task of converting light arriving at the retina into a useful representation that allows us to make sense of the visual environment. We can navigate easily in the three-dimensional world and recognize objects and their properties, even if they appear from different angles and under different lighting conditions. Artificial systems can also perform well on a variety of complex visual tasks. While they may not be as robust and versatile as their biological counterpart, they have surprising capabilities that are rapidly improving. Studying the two types of systems can help us understand what computations enable the transformation of low-level sensory data into an abstract representation. To this end, this dissertation follows three different pathways.
First, we analyze aspects of human perception. The focus is on the perception in the peripheral visual field and the relation to texture perception. Our work builds on a texture model that is based on the features of a deep neural network. We start by expanding the model to the temporal domain to capture dynamic textures such as flames or water. Next, we use psychophysical methods to investigate quantitatively whether humans can distinguish natural textures from samples that were generated by a texture model. Finally, we study images that cover the entire visual field and test whether matching the local summary statistics can produce metameric images independent of the image content.
Second, we compare the visual perception of humans and machines. We conduct three case studies that focus on the capabilities of artificial neural networks and the potential occurrence of biological phenomena in machine vision. We find that comparative studies are not always straightforward and propose a checklist on how to improve the robustness of the conclusions that we draw from such studies.
Third, we address a fundamental discrepancy between human and machine vision. One major strength of biological vision is its robustness to changes in the appearance of image content. For example, for unusual scenarios, such as a cow on a beach, the recognition performance of humans remains high. This ability is lacking in many artificial systems. We discuss on a conceptual level how to robustly disentangle attributes that are correlated during training, and test this on a number of datasets