CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks
Verifying robustness of neural network classifiers has attracted great interests and attention due to the success of deep neural networks and their unexpected vulnerability to adversarial perturbations. Although finding minimum adversarial distortion of neural networks (with ReLU activations) has been shown to be an NP-complete problem, obtaining a non-trivial lower bound of minimum distortion as a provable robustness guarantee is possible. However, most previous works only focused on simple fully-connected layers (multilayer perceptrons) and were limited to ReLU activations. This motivates us to propose a general and efficient framework, CNN-Cert, that is capable of certifying robustness on general convolutional neural networks. Our framework is general – we can handle various architectures including convolutional layers, max-pooling layers, batch normalization layer, residual blocks, as well as general activation functions; our approach is efficient – by exploiting the special structure of convolutional layers, we achieve up to 17 and 11 times of speed-up compared to the state-of-the-art certification algorithms (e.g. Fast-Lin, CROWN) and 366 times of speed-up compared to the dual-LP approach while our algorithm obtains similar or even better verification bounds. In addition, CNN-Cert generalizes state-of-the-art algorithms e.g. Fast-Lin and CROWN. We demonstrate by extensive experiments that our method outperforms state-of-the-art lower-bound-based certification algorithms in terms of both bound quality and speed.
CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks
Akhilan Boopathy1, Tsui-Wei Weng1, Pin-Yu Chen2, Sijia Liu2 and Luca Daniel1 1Massachusetts Institute of Technology, Cambridge, MA 02139 2 MIT-IBM Watson AI Lab, IBM Research
Copyright © 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
|Method||Non-trivial bound||Multi-layer||Scalability & Efficiency||Beyond ReLU||Exploit CNN structure||Pooling and other struc.|
|Reluplex (?), Planet (?)||✓||✓|
|Global Lipschitz constant (?)||✓||✓||✓||✓|
|Local Lipschitz constant (?)||✓||✓||differentiable|
|SDP approach (?)||✓||✓|
|Dual approach (?)||✓||✓||✓|
|Dual approach (?)||✓||✓||codes not yet released||✓||✓|
|Fast-lin / Fast-lip (?)||✓||✓||✓|
|CNN-Cert (This work)||✓||✓||✓||✓||✓||✓|
Recently, studies on adversarial robustness of state-of-the-art machine learning models, particularly neural networks (NNs), have received great attention due to interests in model explainability (?) and rapidly growing concerns on security implications (?). Take object recognition as a motivating example, imperceptible adversarial perturbations of natural images can be easily crafted to manipulate the model predictions, known as prediction-evasive adversarial attacks. One widely-used threat model to quantify the attack strengths is the norm-ball bounded attacks, where the distortion between an original example and the corresponding adversarial example is measured by the norm of their difference in real-valued vector representations (e.g., pixel values for images or embeddings for texts). Popular norm choices are (?), (?), and (?).
The methodology of evaluating model robustness against adversarial attacks can be divided into two categories: game-based or verification-based. Game-based approaches measure the success in mitigating adversarial attacks via mounting empirical validation against a (self-chosen) set of attacks. However, many defense methods have shown to be broken or bypassed by attacks that are adaptive to these defenses under the same threat model (?; ?), and therefore their robustness claims may not extend to untested attacks. On the other hand, verification-based approaches provide certified defense against any possible attacks under a threat model. In the case of an norm-ball bounded threat model, a verified robustness certificate means the (top-1) model prediction on the input data cannot be altered if the attack strength (distortion measured by norm) is smaller than . Different from game-based approaches, verification methods are attack-agnostic and hence can formally certify robustness guarantees, which is crucial to security-sensitive and safety-critical applications.
Although verification-based approaches can provide robustness certification, finding the minimum distortion (i.e., the maximum certifiable robustness) of NNs with ReLU activations has been shown to be an NP-complete problem (?). While minimum distortion can be attained in small and shallow networks (?; ?; ?; ?), these approaches are not even scalable to moderate-sized NNs. Recent works aim to circumvent the scalability issue by efficiently solving a non-trivial lower bound on the minimum distortion (?; ?; ?). However, existing methods may lack generality in supporting different network architectures and activation functions. In addition, current methods often deal with convolutional layers by simply converting back to fully-connected layers, which may lose efficiency if not fully optimized with respect to the NNs, as demonstrated in our experiments. To bridge this gap, we propose CNN-Cert, a general and efficient verification framework for certifying robustness of a broad range of convolutional neural networks (CNNs). The generality of CNN-Cert enables robustness certification of various architectures, including convolutional layers, max-pooling layers batch normalization layers and residual blocks, and general activation functions. The efficiency of CNN-Cert is optimized by exploiting the convolution operation. A full comparison of verification-based methods is given in Table 1.
We highlight the contributions of this paper as follows.
CNN-Cert is general – it can certify robustness on general CNNs with various building blocks, including convolutional/pooling/batch-norm layers and residual blocks, as well as general activation functions such as ReLU, tanh, sigmoid and arctan. Other variants can easily be incorporated. Moreover, certification algorithms Fast-Lin (?) and CROWN (?) are special cases of CNN-Cert.
CNN-Cert is computationally efficient – the cost is similar to forward-propagation as opposed to NP-completeness in formal verification methods, e.g. Reluplex (?). Extensive experiments show that CNN-Cert achieves up to 17 times of speed-up compared to state-of-the-art certification algorithms Fast-Lin and up to 366 times of speed-up compared to dual-LP approaches while CNN-Cert obtains similar or even better verification bounds.
Background and Related Work
Adversarial Attacks and Defenses.
In the white-box setting where the target model is entirely transparent to an adversary, recent works have demonstrated adversarial attacks on machine learning applications empowered by neural networks, including object recognition (?), image captioning (?), machine translation (?), and graph learning (?). Even worse, adversarial attacks are still plausible in the black-box setting, where the adversary is only allowed to access the model output but not the model internals (?; ?; ?; ?). For improving the robustness of NNs, adversarial training with adversarial attacks is by far one of the most effective strategies that showed strong empirical defense performance (?; ?). In addition, verification-based methods have validated that NNs with adversarial training can indeed improve robustness (?; ?).
Robustness Verification for Neural Networks.
Under the norm-ball bounded threat model, for NNs with ReLU activation functions, although the minimum adversarial distortion gives the best possible certified robustness, solving it is indeed computationally intractable due to its NP-completeness complexity (?). Alternatively, solving a non-trivial lower bound of the minimum distortion as a provable robustness certificate is a more promising option but at the cost of obtaining a more conservative robustness certificate. Some analytical lower bounds depending solely on model weights can be derived (?; ?; ?; ?) but they are in general too loose to be useful or limited to 1 or 2 hidden layers. The robustness of NNs can be efficiently certified on ReLU activation (?; ?) and general activation (?) but mostly on models with fully-connected layers. (?) can also be applied to different activation functions but their bound quality might decrease a lot as a trade-off between computational efficiency due to its ‘any-time‘ property. This paper falls within this line of research with an aim of providing both a general and efficient certification framework for CNNs (see Table 1 for detailed comparisons).
Threat model, minimum adversarial distortion and certified lower bound .
Throughout this paper, we consider the norm-ball bounded threat model with full access to all the model parameters. Given an input image and a neural network classifier , let be the class where predicts for . The minimum distortion is the smallest perturbation that results in , and . A certified lower bound satisfies the following: (i) and (ii) for all and , . In other words, a certified bound guarantees a region (an ball with radius ) such that the classifier decision can never be altered for all possible perturbations in that region. Note that is also known as un-targeted robustness, and the targeted robustness is defined as satisfying (i) but with (ii) slightly modified as and , given some targeted class .
CNN-Cert: A General and Efficient Framework for Robustness Certification
|(i) Act-Conv Block|
|(ii) Residual Block|
|(iv) Pooling Block||at location :|
|Note 1: denotes filter coordinate indices and denotes output tensor indices.|
|Note 2: are all tensors. contains only the positive, negative entries of with other entries equal 0.|
|Note 3: for pooling block are slightly different. Please see Appendix (c) for details.|
Overview of our results.
In this section, we present a general and efficient framework CNN-Cert for computing certified lower bounds of minimum adversarial distortion with general activation functions in CNNs. We derive the range of network output in closed-form by applying a pair of linear upper/lower bound on the neurons (e.g. the activation functions, the pooling functions) when the input of the network is perturbed with noises bounded in norm (). Our framework can incorporate general activation functions and various architectures – particularly, we provide results on convolutional layers with activations (a.k.a Act-conv block), max-pooling layers (a.k.a. Pooling block), residual blocks (a.k.a. Residual block) and batch normalization layers (a.k.a. BN block). In addition, we show that the state-of-the-art Fast-Lin algorithm (?) and CROWN (?) are special cases under the CNN-Cert framework.
When an input data point is perturbed within an ball with radius , we are interested in the change of network output because this information can be used to find a certified lower bound of minimum adversarial distortion (as discussed in the section Computing certified lower bound ). Toward this goal, the first step is to derive explicit output bounds for the neural network classifiers with various popular building blocks, as shown in Figure 1, Table 2 and Table 9 (with general strides and padding). The fundamental idea of our method is to apply linear bounding techniques separately on the non-linear operations in the neural networks, e.g. the non-linear activation functions, residual blocks and pooling operations. Our proposed techniques are general and allow efficient computations of certified lower bounds. We begin the formal introduction to CNN-Cert by giving notations and intuitions of deriving explicit bounds for each building block followed by the descriptions of utilizing such explicit bounds to compute certified lower bounds in our proposed framework.
Let be a neural network classifier function and be an input data point. We use to denote the coordinate-wise activation function in the neural networks. Some popular choices of include ReLU: , hyperbolic tangent: , sigmoid: and arctan: . The symbol denotes the convolution operation and denotes the output of -th layer building block, which is a function of an input . We use superscripts to denote index of layers and subscripts to denote upper bound (), lower bound () and its corresponding building blocks (e.g. act is short for activation, conv is short for convolution, res is short for residual block, bn is short for batch normalization and pool is short for pooling). Sometimes subscripts are also used to indicate the element index in a vector/tensor, which is self-content. We will often write as for simplicity and we will sometimes use to denote the output of the classifier, i.e. . Note that the weights , bias , input and the output of each layer are tensors since we consider a general CNN in this paper.
(i) Tackling the non-linear activation functions and convolutional layer.
For the convolutional layer with an activation function , let be the input of activation layer and be the output of convolutional layer. The input/output relation is as follows:
Given the range of , we can bound the range of by applying two linear bounds on each activation function :
When the input is in the range of , the parameters can be chosen appropriately based on ’s lower bound and upper bound . If we use (2) and consider the signs of the weights associated with the activation functions, it is possible to show that the output in (1) can be bounded as follows:
where are constant tensors related to weights and bias as well as the corresponding parameters in the linear bounds of each neuron. See Table 2 for full results. Note the bounds in (3) and (4) are element-wise inequalities and we leave the derivations in the Appendix (a). On the other hand, if is also the output of convolutional layer, i.e.
by letting , , and , . Observe that the form of the upper bound in (5) and lower bound in (6) becomes the same convolution form again as (1). Therefore, for a neural network consists of convolutional layers and activation layers, the above technique can be used iteratively to obtain the final upper and lower bounds of the output in terms of the input of neural network in the following convolutional form:
In fact, the above framework is very general and is not limited to the convolution-activation building blocks. The framework can also incorporate popular residual blocks, pooling layers and batch normalization layers, etc. The key idea is to derive linear upper bounds and lower bounds for each building block in the form of (3) and (4), and then plug in the corresponding bounds and back-propagate to the previous layer.
(ii) Tackling the residual blocks operations.
For the residual block, let denote the output of residual block (before activation) and be the output of first convolutional layer and be the input of residual block. The input/output relation is as follows:
Similar to the linear bounding techniques for up-wrapping the non-linear activation functions, the output of residual block can be bounded as:
where are constant tensors related to weights , , bias , , and the corresponding parameters in the linear bounds of each neuron; see Table 2 for details. Note that in Table 2, all indices are shifted from to . The full derivations are provided in the Appendix (b).
(iii) Tackling the batch normalization.
The batch normalization layer performs operations of scaling and shifting during inference time. Let be the output and be the input, the input/output relation is the following:
where , are the learned training parameters and , are the running average of the batch mean and variance during training. Thus, it is simply scaling and shifting on both upper bounds and lower bounds:
where and .
(iv) Tackling the pooling operations.
Let and be the output and input of the pooling layer. For max-pooling operations, the input/output relation is the following:
where denotes the pooled input index set associated with the -th output. When the input is bounded in the range , it is possible to bound the output by linear functions as follows:
where are constant tensors related to and . For average pooling operation, the range of the output is simply the the average of and on the corresponding pooling indices. See Table 2 and derivation details in Appendix (c).
Computing global bounds and of network output .
Let be the output of a -th layer neural network classifier. We have shown that when the input of each building block is bounded and lies in the range of some , then the output of the building block can be bounded by two linear functions in the form of input convolution. Since a neural network can be regarded as a cascade of building blocks – the input of current building block is the output of previous building block – we can propagate the bounds from the last building block that relates the network output backward to the first building block that relates the network input . A final upper bound and lower bound connect the network output and input are in the following linear relationship:
Recall that the input is constrained within an ball centered at input data point and with radius . Thus, maximizing (minimizing) the right-hand side (left-hand side) of (7) over leads to a global upper (lower) bound of -th output :
where is norm and with .
Computing certified lower bound .
Recall that the predicted class of input data is and let be a targeted class. Given the magnitude of largest input perturbation , we can check if the output by applying the global bounds derived in (8) and (9). In other words, given an , we will check the condition if . If the condition is true, we can increase ; otherwise decrease . Thus, the largest certified lower bound can be attained by a bisection on . Note that although there is an explicit term in (8) and (9), they are not a linear function in because all the intermediate bounds of depend on . Fortunately, we can still find numerically via the aforementioned bisection method. On the other hand, also note that the derivation of output bounds in each building block depend on the range of the building block input (say ), which we call the intermediate bounds. The value of intermediate bounds can be computed similarly by treating as the final output of the sub-network which consists of all building blocks before layer and deriving the corresponding in (7). Thus, all the intermediate bounds also have the same explicit forms as (8) and (9) but substituted by its corresponding .
Discussions: Fast-Lin and CROWN are special cases of CNN-Cert.
Fast-Lin (?) and CROWN (?) are special cases of CNN-Cert. In Fast-Lin, two linear bounds with the same slope (i.e. in (2)) are applied on the ReLU activation while in CROWN and CNN-Cert different slopes are possible ( and can be different). However, both Fast-Lin and CROWN only consider fully-connected layers (MLP) while CNN-Cert can handle various building blocks and architectures such as residual blocks, pooling blocks and batch normalization blocks and is hence a more general framework. We show in Table 13 (appendix) that when using the same linear bounds in ReLU activations, CNN-Cert obtains the same robustness certificate as CROWN; meanwhile, for the general activations, CNN-Cert uses more accurate linear bounds and thus achieves better certificate quality up to 260% compared with CROWN (if we use exactly the same linear bounds, then CNN-Cert and CROWN indeed get the same certificate). Note that in all cases, CNN-Cert is much faster than CROWN (2.5-11.4 speed-up) due to the advantage of explicit convolutional bounds in CNN-Cert.
Discussion: CNN-Cert is computationally efficient.
CNN-Cert has a similar cost to forward-propagation for general convolutional neural networks – it takes polynomial time, unlike algorithms that find the exact minimum adversarial distortion such as Reluplex (?) which is NP-complete. As shown in the experiment sections, CNN-Cert demonstrates an empirical speedup as compared to (a) the original versions of Fast-Lin (b) an optimized sparse matrix versions of Fast-Lin (by us) and (c) Dual-LP approaches while maintaining similar or better certified bounds (the improvement is around 8-20 %). For a pure CNN network with layers, -by- filter size, filters per layer, input size -by-, and stride -by-, the time complexity of CNN-Cert is . The equivalent fully connected network requires time to certify.
Discussion: Training-time operations are independent of CNN-Cert.
Since CNN-Cert is certifying the robustness of a fixed classifier at the testing time, techniques that only apply to the training phase, such as dropout, will not affect the operation of CNN-Cert (though the given model to be certified might vary if model weights differ).
We conduct extensive experiments comparing CNN-Cert with other lower-bound based verification methods on 5 classes of networks: (I) pure CNNs; (II) general CNNs (ReLU) with pooling and batch normalization; (III) residual networks (ReLU); (IV) general CNNs and residual networks with non-ReLU activation functions; (V) small MLP models. Due to page constraints, we refer readers to the appendix for additional results. Our codes are available at https://github.com/AkhilanB/CNN-Cert.
Certification algorithms: (i) Fast-Lin provides certificate on ReLU networks (?); (ii) Global-Lips provides certificate using global Lipschitz constant (?); (iii) Dual-LP solves dual problems of the LP formulation in (?), and is the best result that (?) can achieve, although it might not be attainable due to the any-time property; (iv) Reluplex (?) obtains exact minimum distortion but is computationally expensive.
Robustness estimation, Attack methods: (i) CLEVER (?) is a robustness estimation score without certification; (ii) CW/EAD are attack methods (?; ?).
Our methods: CNN-Cert-Relu is CNN-Cert with the same linear bounds on ReLU used in Fast-Lin, while CNN-Cert-Ada uses adaptive bounds all activation functions. CNNs are converted into equivalent MLP networks before evaluation for methods that only support MLP networks.
Implementations, Models and Dataset. CNN-Cert is implemented with Python (numpy with numba) and we also implement a version of Fast-Lin using sparse matrix multiplication for comparison with CNN-Cert since convolutional layers correspond to sparse weight matrices. Experiments are conducted on a AMD Zen server CPU. We evaluate CNN-Cert and other methods on CNN models trained on the MNIST, CIFAR-10 and tiny Imagenet datasets. All pure convolutional networks use 3-by-3 convolutions. The general 7-layer CNNs use two max pooling layers and uses 32 and 64 filters for two convolution layers each. LeNet uses a similar architecture to LeNet-5 (?), with the no-pooling version applying the same convolutions over larger inputs. The residual networks (ResNet) evaluated use simple residual blocks with two convolutions per block and ResNet with residual blocks is denoted as ResNet-. We evaluate all methods on 10 random test images and attack targets (in order to accommodate slow verification methods) and also 100 images results for some networks in Table 5. It shows that the results of average 100 images are similar to average 10 imagess. We train all models for 10 epochs and tune hyperparameters to optimize validation accuracy.
Results (I): pure CNNs with ReLU activation.
Table 3 demonstrates that CNN-Cert bounds consistently improve on Fast-Lin over network size. CNN-Cert also improves on Dual-LP. Attack results show that all certified methods leave a significant gap on the attack-based distortion bounds (i.e. upper bounds on the minimum distortions). Table 4 gives the runtimes of various methods and shows that CNN-Cert is faster than Fast-Lin, with over an order of magnitude speed-up for the smallest network. CNN-Cert is also faster than the sparse version of Fast-Lin. The runtime improvement of CNN-Cert decreases with network size. Notably, CNN-Cert is multiple orders of magnitude faster than the Dual-LP method. Global-Lips is an analytical bound, but it provides very loose lower bounds by merely using the product of layer weights as the Lipschitz constant. In contrast, CNN-Cert takes into account the network output at the neuron level and thus can certify significantly larger lower bounds, and is around 8-20 % larger compared to Fast-Lin and Dual-LP approaches.
Results (II), (III): general CNNs and ResNet with ReLU activation.
Table 5 gives certified lower bounds for various general CNNs including networks with pooling layers and batch normalization. CNN-Cert improves upon Fast-Lin style ReLU bounds (CNN-Cert-Relu). Interestingly, the LeNet style network without pooling layers has certified bounds much larger than the pooling version while the network with batch normalization has smaller certified bounds. These findings provide some new insights on uncovering the relation between certified robustness and network architecture, and CNN-Cert could potentially be leveraged to search for more robust networks. Table 6 gives ResNet results and shows CNN-Cert improves upon Fast-Lin.
Results (IV): general CNNs and ResNet with general activations.
Table 7 computes certified lower bounds for networks with 4 different activation functions. Some sigmoid network results are omitted due to poor test set accuracy. We conclude that CNN-Cert can indeed efficiently find non-trivial lower bounds for all the tested activation functions and that computing certified lower bounds for general activation functions incurs no significant computational penalty.
Results (V): Small MLP networks.
Table 8 shows results on small MNIST MLP with 20 nodes per layer. For the small 2-layer network, we are able to run Reluplex and compute minimum adversarial distortion. It can be seen that the gap between the certified lower bounds method here are all around 2 times while CLEVER and attack methods are close to Reluplex though without guarantees.
Conclusion and Future Work
In this paper, we propose CNN-Cert, a general and efficient verification framework for certifying robustness of CNNs. By applying our proposed linear bounding technique on each building block, CNN-Cert can handle a wide variety of network architectures including convolution, pooling, batch normalization, residual blocks, as well as general activation functions. Extensive experimental results under four different classes of CNNs consistently validate the superiority of CNN-Cert over other methods in terms of its effectiveness in solving tighter non-trivial certified bounds and its run time efficiency.
Akhilan Boopathy, Tsui-Wei Weng and Luca Daniel are partially supported by MIT-IBM Watson AI Lab.
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(a) Derivation of Act-Conv block:
We are going to show that the output in (1) can be bounded as follows:
where are constant tensors related to weights and bias as well as the corresponding parameters in the linear bounds of each neuron.
Below, we will use subscript to denote the location of and its corresponding weight filter is denoted as . Meanwhile, we will use subscripts to denote the location in the weight filter.
Derivations of upper bounds.
By definition, the -th output is a convolution of previous output with its corresponding filter :
From (10) to (11), we expand the convolution into summation form. From (11) to (12), we apply the linear upper and lower bounds on each activation as described in (2); the inequalities holds when multiplying with a positive weight and will be reversed (the RHS and LHS will be swapped) when multiplying with a negative weight. Since here we are deriving the upper bound, we only need to look at the RHS inequality. This is indeed the key idea for all the derivations. The tensor contains only the positive entries of weights with all others set to zero while contains only the negative entries of and sets other entries to zero. Note that with a slightly abuse of notation, the here are tensors with the same dimensions as (while the in (2) are scalar), and we use subscripts to denote the entry of tensor, e.g. .
Derivations of lower bounds.
The lower bounds can be derived similarly:
(b) Derivation of Residual block:
We are going to show that the output in the residual block can be bounded as follows:
where denote the output of residual block (before activation), be the output of first convolutional layer and be the input of residual block, are constant tensors related to weights , , bias , , and the corresponding parameters in the linear bounds of each neuron. The input/output relation of residual block is as follows:
Below, we will use subscript to denote the location of and its corresponding weight filter is denoted as . Meanwhile, we will use subscripts to denote the location in the weight filter.
Derivations of upper bounds.
Write out and apply the act-conv bound on the term , we obtain
Plug in the equation , we get