A P ANDA ? N O , I T ’ S A S LOTH : S LOWDOWN A TTACKS ON A DAPTIVE M ULTI -E XIT N EURAL N ETWORK I NFERENCE
Anonymous authors
Paper under double-blind review
A BSTRACT
Recent increases in the computational demands of deep neural networks (DNNs), combined with the observation that most input samples require only simple models, have sparked interest in input-adaptive multi-exit architectures, such as MSDNets or Shallow-Deep Networks.
These architectures enable faster inferences and could bring DNNs to low-power devices, e.g. in the Internet of Things (IoT).
However, it is unknown if the computational savings provided by this approach are robust against adversarial pressure.
In particular, an adversary may aim to slow down adaptive DNNs by increasing their average inference time—a threat analogous to the denial-of-service attacks from the Internet.
In this paper, we conduct a systematic evaluation of this threat by experimenting with three generic multi-exit
DNNs (based on VGG16, MobileNet, and ResNet56) and a custom multi-exit architecture, on two popular image classiﬁcation benchmarks (CIFAR-10 and Tiny
ImageNet).
To this end, we show that adversarial sample-crafting techniques can be modiﬁed to cause slowdown, and we propose a metric for comparing their impact on different architectures.
We show that a slowdown attack reduces the efﬁcacy of multi-exit DNNs by 90%–100%, and it ampliﬁes the latency by 1.5–5 × in a typical IoT deployment.
We also show that it is possible to craft universal, reusable perturbations and that the attack can be effective in realistic black-box scenarios, where the attacker has limited knowledge about the victim.
Finally, we show that adversarial training provides limited protection against slowdowns.
These results suggest that further research is needed for defending multi-exit architectures against this emerging threat.
Our code is available at https://anonymized-repository .
I NTRODUCTION
The inference-time computational demands of deep neural networks (DNNs) are increasing, owing to the “going deeper" (Szegedy et al., 2015) strategy for improving accuracy: as a DNN gets deeper, it progressively gains the ability to learn higher-level, complex representations.
This strategy has enabled breakthroughs in many tasks, such as image classiﬁcation (Krizhevsky et al., 2012) or speech recognition (Hinton et al., 2012), at the price of costly inferences.
For instance, with 4 × more inference cost, a 56-layer ResNet (He et al., 2016) improved the Top-1 accuracy on ImageNet by 19% over the 8-layer AlexNet.
This trend continued with the 57-layer state-of-the-art EfﬁcientNet (Tan &
Le, 2019): it improved the accuracy by 10% over ResNet, with 9 × costlier inferences.
The accuracy improvements stem from the fact that the deeper networks ﬁx the mistakes of the shallow ones (Huang et al., 2018).
This implies that some samples, which are already correctly classiﬁed by shallow networks, do not necessitate the extra complexity.
This observation has motivated research on input-adaptive mechanisms, in particular, multi-exit architectures (Teerapittayanon et al., 2016;
Huang et al., 2018; Kaya et al., 2019; Hu et al., 2020).
Multi-exit architectures save computation by making input-speciﬁc decisions about bypassing the remaining layers, once the model becomes conﬁdent, and are orthogonal to techniques that achieve savings by permanently modifying the model (Li et al., 2016; Banner et al., 2018; Han et al., 2015; Taylor et al., 2018).
Figure 1 illustrates how a multi-exit model (Kaya et al., 2019), based on a standard VGG-16 architecture, correctly classiﬁes a selection of test images from ‘Tiny ImageNet’ before the ﬁnal layer.
We see that more typical samples, which have more supporting examples in the training set, require less depth and, therefore, less computation.
1 st 5 th 14 th
L e m o n
L i o n
It is unknown if the computational savings provided by multi-exit architectures are robust against adversarial pressure.
Prior research showed that DNNs are vulnerable to a wide range of attacks, which involve imperceptible input perturbations (Szegedy et al., 2014; Goodfellow et al., 2015;
Papernot et al., 2016; Hu et al., 2020).
Considering that a multi-exit model, on the worst-case input, does not provide any computational savings, we ask: Can the savings from multi-exit models be maliciously negated by input perturbations?
As some natural inputs do require the full depth of the model, it may be possible to craft adversarial examples that delay the correct decision; it is unclear, however, how many inputs can be delayed with imperceptible perturbations.
Furthermore, it is unknown if universal versions of these adversarial examples exist, if the examples transfer across multi-exit architectures and datasets, or if existing defenses (e.g. adversarial training) are effective against slowdown attacks.
Threat Model.
We consider a new threat against DNNs, analogous to the denial-of-service (DoS) attacks that have been plaguing the Internet for decades.
By imperceptibly perturbing the input to trigger this worst-case, the adversary aims to slow down the inferences and increase the cost of using the DNN.
This is an important threat for many practical applications, which impose strict limits on the responsiveness and resource usage of DNN models (e.g. in the Internet-of-Things (Taylor et al., 2018)), because the adversary could push the victim outside these limits.
For example, against a commercial image classiﬁcation system, such as Clarifai.com, a slowdown attack might waste valuable computational resources.
Against a model partitioning scheme, such as Big-Little (De Coninck et al., 2015), it might introduce network latency by forcing excessive transmissions between local and remote models.
A slowdown attack aims to force the victim to do more work than the adversary, e.g. by amplifying the latency needed to process the sample or by crafting reusable perturbations.
The adversary may have to achieve this with incomplete information about the multi-exit architecture targeted, the training data used by the victim or the classiﬁcation task (see discussion in Appendix A).
Our Contributions.
To our best knowledge, we conduct the ﬁrst study of the robustness of multi-exit architectures against adversarial slowdowns.
To this end, we ﬁnd that examples crafted by prior evasion attacks (Madry et al., 2017; Hu et al., 2020) fail to bypass the victim model’s early exits, and we show that an adversary can adapt such attacks to the goal of model slowdown by modifying its objective function.
We call the resulting attack DeepSloth .
We also propose an efﬁcacy metric for comparing slowdowns across different multi-exit architectures.
We experiment with three generic multi-exit DNNs (based on VGG16, ResNet56 and MobileNet)
(Kaya et al., 2019) and a speciallydesigned multi-exit architecture, MSDNets (Huang et al., 2018), on two popular image classiﬁcation benchmarks (CIFAR-10 and Tiny ImageNet).
We ﬁnd that DeepSloth reduces the efﬁcacy of multiexit DNNs by 90%–100%, i.e. , the perturbations render nearly all early exits ineffective.
In a scenario typical for IoT deployments, where the model is partitioned between edge devices and the cloud, our attack ampliﬁes the latency by 1.5–5 × , negating the beneﬁts of model partitioning.
We also show that it is possible to craft a universal DeepSloth perturbation, which can slow down the model on either all or a class of inputs.
While more constrained, this attack still reduces the efﬁcacy by 5%–45%.
Further, we observe that DeepSloth can be effective in some black-box scenarios, where the attacker has limited knowledge about the victim.
Finally, we show that a standard defense against adversarial samples—adversarial training—is inadequate against slowdowns.
Our results suggest that further research will be required for protecting multi-exit architectures against this emerging security threat.
R ELATED W ORK
Adversarial Examples and Defenses.
Prior work on adversarial examples has shown that DNNs are vulnerable to test-time input perturbations (Szegedy et al., 2014;
Goodfellow et al., 2015; Papernot et al., 2017; Carlini & Wagner, 2017; Madry et al., 2018).
An adversary who wants to maximize a model’s error on speciﬁc test-time samples can introduce human-imperceptible perturbations to these samples.
Moreover, an adversary can also use a surrogate model for launching the attack and still hurt an unknown victim (Athalye et al., 2018; Tramèr et al., 2017b; Inkawhich et al., 2019).
This transferability leads to adversarial examples in more practical black-box scenarios.
Although many defenses (Kurakin et al., 2016; Xu et al., 2017; Song et al., 2018; Liao et al., 2018; Lecuyer et al., 2019) have been proposed against this threat, adversarial training (AT) have become the frontrunner (Madry et al., 2018).
In Sec 5 we evaluate the vulnerability of multi-exit DNNs to slowdowns in white-box and black-box scenarios; in Sec 6, we show that standard AT and its simple adaptation to our perturbations are not sufﬁcient for preventing slowdown attacks.
Efﬁcient Input-Adaptive Inference.
Recent input-adaptive DNN architectures have brought two seemingly distant goals closer: achieving both high predictive quality and computational efﬁciency.
There are two types of input-adaptive DNNs: adaptive neural networks (AdNNs) and multi-exit architectures.
During the inference, AdNNs (Wang et al., 2018; Figurnov et al., 2017) dynamically skip a certain part of the model to reduce the number of computations.
This mechanism can be used only for ResNet-based architectures to employ skipping within a network.
However, multi-exit architectures (Teerapittayanon et al., 2016; Huang et al., 2018; Kaya et al., 2019) introduce multiple side branches—or early-exits—to a model.
During the inference on an input sample, these models can preemptively stop the computation once the stopping criteria are met at one of the branches.
Haque et al. (2020) presented attacks speciﬁcally designed for reducing the energy-efﬁciency of AdNNs by using adversarial input perturbations.
However, our work studies a new threat model that an adversary aims to cause slowdowns on multi-exit architectures.
By imperceptibly perturbing the inputs, our attacker can (i) introduce network latency to an infrastructure that utilizes multiexit architectures, and (ii) waste the victim’s computational resources.
To this end, we deﬁne a new metric to measure the impact of any adversarial input perturbation on multi-exit architectures (Sec 3).
In Sec 5, we also study practical attack scenarios and transferability of adversarial input perturbations crafted by our attacker.
Moreover, we discuss the potential defense mechanisms against this vulnerability, by proposing a simple adaptation of adversarial training (Sec 6).
To the best of our knowledge, our work is the ﬁrst systematic study of this new vulnerability.
Model Partitioning.
Model partitioning has been proposed to bring DNNs to resource-constrained devices (De Coninck et al., 2015; Taylor et al., 2018).
These schemes split a multi-exit model into sequential components and deploy them in separate endpoints, e.g., a small, local on-device part and a large, cloud-based part.
For bringing DNNs to the Internet of Things (IoT), partitioning is instrumental as it reduces the transmissions between endpoints, a major bottleneck.
In Sec 5.1, on a partitioning scenario, we show that our attack can force excessive transmissions.
E XPERIMENTAL S ETUP
Datasets.
We use two datasets: CIFAR-10 (Krizhevsky et al., 2009) and Tiny-ImageNet (Tiny).
For testing the cross-domain transferability of our attacks, we use the CIFAR-100 dataset.
Architectures and
To demonstrate that the vulnerability to adversarial slowdowns is common among multi-exit architectures, we experiment on two recent techniques: Shallow-Deep Networks (SDNs) (Kaya et al., 2019) and MSDNets (Huang et al., 2018).
These architectures were designed for different purposes: SDNs are generic and can convert any
DNN into a multi-exit model, and MSDNets are custom designed for efﬁciency.
We evaluate an
MSDNet architecture (6 exits) and three SDN architectures, based on VGG-16 (Simonyan & Zisserman, 2014) (14 exits), ResNet-56 (He et al., 2016) (27 exits), and MobileNet (Howard et al., 2017) (14 exits).
Metrics.
We deﬁne the early-exit capability (EEC) curve of a multi-exit model to indicate the fraction of the test samples that exit early at a speciﬁc fraction of the model’s full inference cost.
Figure 2 shows the EEC curves of our SDNs on Tiny
ImageNet, assuming that the computation stops when there is a correct classiﬁcation at an exit point.
For example, the VGG-16 model can correctly classify ∼ 50% of the samples using ∼ 50% of its full cost.
Note that this stopping criterion is impractical; in Sec 4, we will discuss the practical ones.
We deﬁne the early-exit efﬁcacy, or efﬁcacy in short, to quantify a model’s ability of utilizing its exit points.
The efﬁcacy of a multi-exit model is the area under its EEC curve, estimated via the trapezoidal rule.
An ideal efﬁcacy for a model is close to 1 , when most of the input samples the computation stops very early; models that do not use their early exits have 0 efﬁcacy.
A model with low efﬁcacy generally exhibits a higher latency ; in a partitioned model, the low efﬁcacy will cause more input transmissions to the cloud, and the latency is further ampliﬁed by the network round trips.
A multi-exit model’s efﬁcacy and accuracy are dictated by its stopping criteria, which we discuss in the next section.
As for the classiﬁcation performance, we report the Top-1 accuracy on the test data.
A TTACKING THE M ULTI -E XIT A RCHITECTURES
Setting.
We consider the supervised classiﬁcation setting with standard feedforward DNN architectures.
A DNN model consists of N blocks, or layers, that process the input sample, x ∈ R d , from beginning to end and produce a classiﬁcation.
A classiﬁcation, F ( x, θ ) ∈ R m , is the predicted probability distribution of x belonging to each label y ∈ M = { 1 ...m } .
Here, θ denotes the tunable parameters, or the weights, of the model.
The parameters are learned on a training set D that contains multiple ( x i , y i ) pairs; where y i is the ground-truth label of the training sample x i .
We use θ i to denote the parameters at or before the i th block; i.e., θ i ⊂
θ i +
and θ N = θ .
Multi-Exit Architectures.
A multi-exit model contains K exit points —internal classiﬁers—attached to a model’s hidden blocks.
We use F i to denote the i th exit point, which is attached to the j th block.
Using the output of the j th ( j < N ) block on x , F i produces an internal classiﬁcation, i.e., F i ( x, θ j ) , which we simply denote as F i ( x ) .
In our experiments, we set K = N for SDNs, i.e., one internal classiﬁer at each block and K = 6 for MSDNets.
Given F i ( x ) , a multi-exit model uses deterministic criteria to decide between forwarding x to compute F i +1 ( x ) and stopping for taking the early-exit at this block.
Bypassing early-exits decreases a network’s efﬁcacy as each additional block increases the inference time.
Note that multi-exit models process each sample individually, not in batches.
Practical Stopping Criteria.
Ideally, a multi-exit model stops when it reaches a correct classiﬁcation at an exit point, i.e., argmax j ∈ M F ( j ) i ( x )
=
ˆ y i
=
y ; y is the ground-truth label.
However, for unseen samples, this is impractical as y is unknown.
The prior work has proposed two simple strategies to judge whether ˆ y i
=
y : F i ( x ) ’s entropy (Huang et al., 2018; Teerapittayanon et al., 2016) or its conﬁdence (Kaya et al., 2019).
Our attack (see Sec 4.3) leverages the fact that a uniform F i ( x ) has both the highest entropy and the lowest conﬁdence.
For generality, we experiment with both conﬁdence-based—SDNs—and entropy-based—MSDNets—strategies.
A strategy selects conﬁdence, or entropy, thresholds, T i , that determine whether the model should take the i th exit for an input sample.
Conservative T i ’s lead to fewer early exits and the opposite hurts the accuracy as the estimate of whether ˆ y i
=
y becomes unreliable.
As utility is a major practical concern, we set T i ’s for balancing between efﬁciency and accuracy.
On a holdout set, we set the thresholds to maximize a model’s efﬁcacy while keeping its relative accuracy drop (RAD) over its maximum accuracy within 5% and 15%.
We refer to these two settings as RAD < 5%
and RAD < 15%.
Table 2 ( ﬁrst segment) shows how accuracy and efﬁcacy change in each setting.
4. T HREAT M ODEL
We consider an adversary who aims to decrease the early-exit efﬁcacy of a victim model.
The attacker crafts an imperceptible adversarial perturbation, v ∈ R d that, when added to a test-time sample x ∈ S , prevents the model from taking early-exits.
Adversary’s Capabilities.
The attacker is able to modify the victim’s test-time samples to apply the perturbations, e.g., by compromising a camera that collects the data for inference.
To ensure the imperceptibility, we focus on (cid:96) ∞ norm bounded perturbations as they (i) are well are studied; (ii) have successful defenses (Madry et al., 2018); (iii) have prior extension to multi-exit models (Hu et al., 2020); and (iv) are usually the most efﬁcient to craft.
We show results on (cid:96) 2 and (cid:96) 1 perturbations in Appendix C. In line with the prior work, we bound the perturbations as follows: for CIFAR-10,
Adversary’s Knowledge.
To assess the security vulnerability of multi-exit architectures, we study white-box scenarios , i.e., the attacker knows all the details of the victim model, including its D and θ .
Further, in Sec 5.2, we study more practical black-box scenarios , i.e., the attacker crafts v on a surrogate model and applies it to an unknown victim model.
Adversary’s Goals.
We consider three DeepSloth variants, (i) the standard , (ii) the universal and (iii) the class-universal .
The adversary, in (i) crafts a different v for each x ∈ S ; in (ii) crafts a single v for all x ∈ S ; in (iii) crafts a single v for a target class i ∈ M .
Further, although the adversary does not explicitly target it; we observe that DeepSloth usually hurts the accuracy.
By modifying the objective function we describe in Sec 4.3, we also experiment with DeepSloth variants that can explicitly preserve or hurt the accuracy, in addition to causing slowdowns.
4. S TANDARD A DVERSARIAL A TTACKS D O N OT C AUSE D ELAYS
To motivate DeepSloth, we ﬁrst evaluate whether previous adversarial attacks have any effect on the efﬁcacy of multi-exit models.
These attacks add imperceptible perturbations to a victim’s test-time samples to force misclassiﬁcations.
We experiment with the standard PGD attack (Madry et al., 2017); PGD-avg and PGD-max variants against multi-exit models (Hu et al., 2020) and the Universal
Adversarial Perturbation (UAP) attack that crafts a single perturbation for all test samples (MoosaviDezfooli et al., 2017).
Table 1 summarizes our ﬁndings that these attacks, although they hurt the accuracy, fail to cause any meaningful decrease in efﬁcacy.
In many cases, we observe that the attacks actually increase the efﬁcacy.
These experiments help us to identify the critical elements of the objective function of an attack that decreases the efﬁcacy.
[Table]
The Layer-Wise Objective Function.
Figure 3 shows that the attacks that only optimize for the ﬁnal output, e.g., PGD or UAP, does not perturb the model’s earlier layer representations.
This does not bypass the early-exits, which makes these attacks ineffective for decreasing the efﬁcacy.
Therefore, we modify the objective functions of adversarial example crafting algorithms to incorporate the outputs of all F i | i < K .
For crafting (cid:96) ∞ , (cid:96) 2 and (cid:96) 1 -bounded perturbations, we adapt the PGD (Madry et al., 2017), the DDN (Rony et al., 2019) and the SLIDE algorithms (Tramèr & Boneh, 2019), respectively.
Next, we describe how we modify the PGD algorithm—we modify the others similarly:
[Equation]
Here, t is the current attack iteration; α is the step size; Π is the projection operator that enforces || v || ∞ < (cid:15) and L is the cross-entropy loss function.
The selection of D (cid:48) determines the type of the attack.
For the standard variant: D (cid:48)
= { x } , i.e., a single test-time sample.
For the universal variant:
D (cid:48)
=
D , i.e., the whole training set.
For the class-universal variant against the target class i ∈ M :
D (cid:48)
= { ( x, y ) ∈
D| y
= i } , i.e., the training set samples from the i th class.
Finally, ¯ y is the target label distribution our objective pushes F i ( x ) towards.
Next, we explain how we select ¯ y .
Pushing F i ( x ) Towards a Uniform Distribution.
Despite including all F i , attacks such as PGDavg and PGD-max (Hu et al., 2020) still fail to decrease efﬁcacy.
How these attacks select ¯ y reﬂects their goal of causing misclassiﬁcations and, therefore, they trigger errors in early-exits, i.e., argmax j ∈ M F ( j ) i ( x ) =
¯ y (cid:54)
=
y .
However, as the early-exits still have high conﬁdence, or low entropy, the model still stops its computation early.
We select ¯ y as a uniform distribution over the class labels, i.e., ¯ y ( i )
= 1 /m .
This ensures that ( x + v ) bypasses common stopping criteria as a uniform F i ( x ) has both the lowest conﬁdence and the highest entropy.
E MPIRICAL E VALUATION
Here, we present the results for (cid:96) ∞
DeepSloth against two SDNs—VGG-16 and MobileNet-based— and against the MSDNets.
In the Appendix, we report the hyperparameters; the (cid:96) 1 and (cid:96) 2 attacks; the results on ResNet-56-based SDNs; the cost of the attacks; and some perturbed samples.
Overall, we observe that (cid:96) ∞
-bounded perturbations are more effective for slowdowns.
The optimization challenges might explain this, as (cid:96) 1 and (cid:96) 2 attacks are usually harder to optimize (Carlini & Wagner, 2017; Tramèr & Boneh, 2019).
Unlike objectives for misclassiﬁcations, the objective for slowdowns involves multiple loss terms and optimizes over all the output logits.
5. W HITE -B OX S CENARIOS
Perturbations Eliminate Early-Exits.
Table 2 ( second segment) shows that the victim models have ∼ 0 efﬁcacy on the samples perturbed by DeepSloth.
Across the board, the attack makes the early-exit completely ineffective and force the victim models to forward all inputs samples till the end.
Moreover, DeepSloth also drops the victim’s accuracy by 75-99%, comparable to the PGD attack.
These results give an answer to our main research question: the multi-exit mechanisms are vulnerable and their beneﬁts can be maliciously offset by input perturbations .
Note that crafting a single perturbation requires multiple back-propagations through the model and more ﬂoating points operations ( FLOPs ) than the forward pass.
The high cost of crafting, relative to the computational damage to the victim, might make this vulnerability unattractive for the adversary.
In the next sections, we highlight scenarios where this vulnerability might lead to practical exploitation.
First, we show that in an IoT-like scenarios, the input transmission is a major bottleneck and DeepSloth can exploit it.
Second, we evaluate universal DeepSloth attacks that enable the adversary to craft the perturbation only once and reuse it on multiple inputs.
[Table] [Table]
Attacking an IoT Scenario.
Many IoT scenarios, e.g., health monitoring for elderly (Park et al., 2017), require collecting data from edge devices and making low-latency inferences on this data.
However, complex deep learning models are impractical for low-power edge devices, such as an
Arduino, that are common in the IoT scenarios (Chen & Ran, 2019).
For example, on standard hardware, an average inference takes MSDNet model on Tiny ImageNet 35M FLOPs and ∼ 10ms.
A potential solution is sending the inputs from the edge to a cloud model, which then returns the prediction.
Even in our optimistic estimate with a nearby AWS EC2 instance, this back-and-forth introduces ∼ 11ms latency per inference.
Model partitioning alleviates this bottleneck by splitting a multi-exit model into two; deploying the small ﬁrst part at the edge and the large second part at the cloud (De Coninck et al., 2015).
The edge part sends an input only when its prediction does not meet the stopping criteria.
For example, the ﬁrst early-exit of MSDNets sends only 5% and 67% of all test samples, on CIFAR-10 and Tiny ImageNet, respectively.
This leads to a lower average latency per inference, i.e., from 11ms down to 0.5ms and 7.4ms, respectively.
The adversary we study uses DeepSloth perturbations to force the edge part to send all the input samples to the cloud.
For the victim, we deploy MSDNet models that we split into two parts at their ﬁrst exit point.
Targeting the ﬁrst part with DeepSloth forces it to send 96% and 99.97% of all test samples to the second part.
This increases average inference latency to ∼ 11ms and invalidates the beneﬁts of model partitioning.
In this scenario, perturbing each sample takes ∼ 2ms on a Tesla VGPU, i.e., the time adversary spends is ampliﬁed by 1.5-5 × as the victim’s latency increase.
Reusable Universal Perturbations.
The universal attacks, although limited, are a practical as the adversary can reuse the same perturbation indeﬁnitely to cause minor slowdowns.
Table 2 ( third segment) shows that they decrease the efﬁcacy by 3-21% and the accuracy by 15-25%, over the baselines.
Having a less conservative early-exit strategy, e.g., RAD < 15%, increases the resilience to the attack at the cost of accuracy.
Further, MSDNets are fairly resilient with only 3-9% efﬁcacy drop; whereas SDNs are more vulnerable with 12-21% drop.
The attack is also slightly more effective on the more complex task, Tiny ImageNet, as the early-exits become easier to bypass.
Using random noise as a baseline, i.e., v ∼ U d ( − (cid:15), (cid:15) ) , we ﬁnd that at most it decreases the efﬁcacy by ∼ 3%.
In the universal attack, we observe a phenomenon: it pushes the samples towards a small subset of all classes.
For example, ∼ 17% of the perturbed samples are classiﬁed into the ’bird’ class of CIFAR-10; up from ∼ 10% for the clean samples.
Considering certain classes are distant in the feature space, e.g. ’truck’ and ’bird’; we expect the class-universal variant to be more effective.
The results in
Table 2 ( fourth segment) conﬁrm our intuition.
We see that this attack decreases the baseline efﬁcacy by 8%-50% and the accuracy by 50%-65%.
We report the average results across multiple classes; however, we observe that certain classes are slightly more vulnerable to this attack.
[Figure]
Feature Visualization of DeepSloth.
In Figure 3, to shed light on how DeepSloth differs from prior attacks, e.g., PGD and PGD-avg, we visualize a model’s hidden block (layer) features on the original and perturbed test-time samples.
We observe that in an earlier block ( left panel), DeepSloth seems to disrupt the original features slightly more than the PGD attacks.
Leaving earlier representations intact prevents PGDs from bypassing the early-exits.
The behaviors of the attacks diverge in the middle blocks ( middle panel).
Here, DeepSloth features remain closer to the original features than prior attacks.
The signiﬁcant disruption of prior attacks leads to high-conﬁdence misclassiﬁcations and fails to bypass early-exits.
In the later block ( right panel), we see that the divergent behavior persists.
Preserving or Hurting the Accuracy with DeepSloth.
Here, we aim to answer whether DeepSloth can be applied when the adversary explicitly aims to cause or prevent misclassiﬁcations, while still causing slowdowns.
Our main threat model has no explicit goal regarding misclassiﬁcations that hurt the user of the model, i.e., who consumes the output of the model.
Whereas, slowdowns additionally hurt the executor or the owner of the model through the computations and latency increased at the cloud providers.
In some ML-in-the-cloud scenarios, where these two are different actors, the adversary might aim to target only the executor or both the executor and the user.
To this end, we modify our objective function slightly to push F i ( x ) towards a slightly non-uniform distribution, favoring either the ground truth label for preventing misclassiﬁcations or a wrong label for causing them.
We test this idea on our VGG-16-based SDN model on CIFAR-10 in RAD < 5% setting.
We see that DeepSloth for preserving the accuracy leads to 81% accuracy with 0.02 efﬁcacy and DeepSloth for hurting the accuracy leads to 4% accuracy with 0.01 efﬁcacy—the original DeepSloth led to 13% accuracy with 0.01 efﬁcacy.
These results show the ﬂexibility of DeepSloth and how it could be modiﬁed depending on the attacker’s goals.
5. T OWARDS A B LACK -B OX A TTACK : T RANSFERABILITY OF D EEP S LOTH
Transferability of adversarial examples imply that they can still hurt a model that they were not crafted on (Tramèr et al., 2017a;
Liu et al., 2017).
Even though white-box attacks are important to expose the vulnerability, black-box attacks, by requiring fewer assumptions, are more practical.
Here, on four distinct scenarios, we investigate whether DeepSloth is transferable.
Based on the scenario’s constraints, we (i) train a surrogate model; (ii) craft the DeepSloth samples on it; (iii) use these samples on the victim.
We run these experiments on CIFAR-10 in the RAD < 5%.
Cross-Architecture.
First, we relax the assumption that the attacker knows the victim architecture.
We evaluate the transferability between a VGG-16-based SDN an an MSDNet—all trained using the same D .
We ﬁnd that the samples crafted on the MSDNet can slowdown the SDN: reducing its efﬁcacy to 0.63 (from 0.77) and accuracy to 78% (from 88%).
Interestingly, the opposite seems not to be the case: on the samples crafted against the SDN, the MSDNet still has 0.87 efﬁcacy (from 0.89) and 73% accuracy (from 85%).
This hints that DeepSloth transfers if the adversary uses an effective multi-exit models as the surrogate.
Limited Training Set Knowledge.
Second, we relax the assumption that the attacker knows the victim’s training set, D .
Here, the attacker only knows a random portion of D , i.e., 10%, 25%, and 50%.
We use VGG-16 architecture for both the surrogate and victim models.
In the 10%, 25% and 50% settings, respectively, the attacks reduce the victim’s efﬁcacy to 0.66, 0.5, 0.45 and 0.43 (from 0.77); its accuracy to 81%, 73%, 72% and 74% (from 88%).
Overall, the more limited the adversary’s
D is, the less generalization ability the surrogate has and the less transferable the attacks are.
Cross-Domain.
Third, we relax the assumption that the attacker exactly knows the victim’s task.
Here, the attacker uses D ∫ to train the surrogate, different from the victim’s D altogether.
We use a
VGG-16 on CIFAR-100 as the surrogate and attack a VGG-16-based victim model on CIFAR-10.
This transfer attack reduces the victim’s efﬁcacy to 0.63 (from 0.77) and its accuracy to 83% (from 88%).
We see that the cross-domain attack might be more effective than the limited D scenarios.
This makes DeepSloth particularly dangerous as the attacker, without knowing the victim’s D , can collect a similar dataset and still slow down the victim.
We hypothesize the transferability of earlier layer features in CNNs (Yosinski et al., 2014) enables the perturbations attack to transfer from one domain to another, as long as they are similar enough.
Cross-Mechnanism.
Finally, we test the scenario where the victim uses a completely different mechanism than a multi-exit architecture to implement input adaptiveness, i.e., SkipNet (Wang et al., 2018).
A SkipNet, a modiﬁed residual network, selectively skips convolutional blocks based on the activations of the previous layer and, therefore, does not include any internal classiﬁers.
We use a pre-trained SkipNet on CIFAR-10 that reduces the average computation for each input sample by ∼ 50% over an equivalent ResNet and achieves ∼ 94% accuracy.
We then feed DeepSloth samples crafted on a MSDNet to this SkipNet, which reduces its average computational saving to ∼ 32% (36% less effective) and its accuracy to 37%.
This result suggests that the two different mechanisms have more in common than previously known and might share the vulnerability.
We believe understanding the underlying mechanisms through which adaptive models save computation is an important research question for future research.
S TANDARD A DVERSARIAL T RAINING I S N OT A C OUNTERMEASURE
In this section, we examine whether a defender can adapt a standard countermeasure against adversarial perturbations, adversarial training (AT) (Madry et al., 2018), to mitigate our attack.
AT decreases a model’s sensitivity to perturbations that signiﬁcantly change the model’s outputs.
While this scheme is effective against adversarial examples that aim to trigger misclassiﬁcations; it is unclear whether using our DeepSloth samples for AT can also robustify a multi-exit model against slowdown attacks.
To evaluate, we train our multi-exit models as follows.
We ﬁrst take a base network—VGG-16—and train it on CIFAR-10 on PGD-10 adversarial examples.
We then convert the resulting model into a multi-exit architecture, using the modiﬁcation from (Kaya et al., 2019).
During this conversion, we adversarially train individual exit points using PGD-10, PGD-10 (avg.), PGD-10 (max.)
[Table]
and
DeepSloth; similar to (Hu et al., 2020).
Finally, we measure the efﬁcacy and accuracy of the trained models against PGD-20, PGD-20 (avg.), PGD-20 (max.), and DeepSloth, on CIFAR-10’s test-set.
[Table]
Our results in Table 3 verify that AT provides resilience against all PGD attacks.
Besides, AT provides some resilience to our attack: DeepSloth reduces the efﬁcacy to ∼ 0.32 on robust models vs. 0.
on the undefended one.
However, we identify a trade-off between the robustness and efﬁciency of multi-exits.
Compared to the undefended model, on clean samples, we see that robust models have lower efﬁcacy—0.77 vs. 0.53 ∼ 0.61.
We observe that the model trained only with our DeepSloth samples (Ours) can recover the efﬁcacy on both the clean and our DeepSloth samples, but this model loses its robustness against PGD attacks.
Moreover, when we train a model on both our DeepSloth samples and PGD-10 (Ours + PGD-10), the trained model suffers from low efﬁcacy.
Our results imply that a defender may require an out-of-the-box defense, such as ﬂagging the users whose queries bypass the early-exits more often than clean samples for which the multi-exit network was calibrated.
C ONCLUSIONS
This work exposes the vulnerability of input-adaptive inference mechanisms against adversarial slowdowns.
As a vehicle for exploring this vulnerability systematically, we propose DeepSloth, an attack that introduces adversarial perturbations to test-time inputs for offsetting the computational beneﬁts of multi-exit inference mechanisms.
We show that a white-box attack, which perturbs each sample individually, eliminates any computational savings these mechanisms provide.
We also show that it is possible to craft universal slowdown perturbations, which can be reused, and transferrable samples, in a black-box setting.
Moreover, adversarial training, a standard countermeasure for adversarial perturbations, is not effective against DeepSloth.
Our analysis suggests that slowdown attacks are a realistic, yet under-appreciated, threat against adaptive models.
A M OTIVATING E XAMPLES
Here we discuss two exemplary scenarios where an adversary can exploit the slowdown attacks.
• (Case 1) Attacks on cloud-based IoT applications.
In most cases, cloud-based IoT applications, such as Apple Siri, Google Now, or Microsoft Cortana, run their DNN inferences in the cloud.
This cloud-only approach increases both the amount of data to be sent to the cloud and computational pressures on cloud servers.
To address this issue, recent work (Kang et al., 2017; Li et al., 2018;
Zhou et al., 2019) utilizes multi-exit architectures for bringing computationally expensive models running on the cloud, such as language models (Zhou et al., 2020; Hou et al., 2020), to mobile (or IoT) devices on the edge.
For instance, they partition a multi-exit model into two components and deploy them on a server and IoT devices, respectively.
Thus, they can reduce the computations performed in both the cloud and edge devices and also decrease the amount of data to be sent to.
However, our adversary aims to offset the computational and communication savings by exploiting adversarial input perturbations.
The adversarial examples crafted by our attacker make inputs skip early-exits and in consequence, cause the slowdowns to cloud-based IoT applications.
Here, a defender may deploy DoS defenses such as ﬁrewalls or rate-limiting.
In this setting, the attacker may not cause DDoS since defenses limit the communication between the server and clients under a certain-level.
Nevertheless, our attacker still increases: (i) the computations at the edge (by making inputs skip early-exits) and (ii) the number of samples that cloud servers are processing.
Remind that a VGG-16 SDN model classiﬁes 90% of clean CIFAR-10 instances correctly at the ﬁrst exit.
Therefore, if the samples crafted by our attacker can only bypass the ﬁrst exit can easily increase the computations on IoT devices and make them send requests to the cloud.
• (Case 2)
Attacks on real-time DNN inference for resource- and time-constrained scenarios.
Recent work on the real-time systems (Hu et al.,
; Jiang et al., 2019) harnesses multi-exit architectures and model partitioning as a solution to optimize real-time DNN inference for resourceand time-constrained scenarios.
In particular, Hu et al.
(2019) showed a real-world prototype of an optimal model partitioning, which is based on a self-driving car video dataset, can improve latency and throughput of partitioned models on the cloud and edge by 6.5-14 × , respectively.
However, the prior work does not consider the danger of slowdown attacks; our threat model has not been discussed before in the literature.
Our results in Sec 5 suggest that slowdown can be induced adversarially, potentially violating real-time guarantees.
For example, our attacker can make partitioned models on the cloud and edge use maximal computations for inference.
Further, the same adversarial examples also require the inference results from the model running on the cloud, which potentially increases the response time of the edge devices by 1.5-5 × .
Thus, we showed that multi-exit architectures should be used with caution in real-time systems.
B H YPERPARAMETERS
In our experiments, we use the following hyperparameters to craft adversarial perturbations.
(cid:96) ∞
-based DeepSloth.
We ﬁnd that (cid:96) ∞ -based
DeepSloth does not require careful tuning.
For the standard attack, we set the total number of iterations to 30 and the step size to α = 0 .
For the modiﬁed attacks for hurting or preserving the accuracy, we set the total number of iterations toand the step size to α = 0 .
We compute the standard perturbations using the entire 10k test-set samples in CIFAR-10 and Tiny Imagenet.
For the universal variants, we set the total number of iterations to 12 and reduce the initial step size of α = 0 .
005 by a factor of 10 every 4 iterations.
To compute a universal perturbation, we use randomly chosen 250 (CIFAR-10) and 200 (Tiny Imagenet) training samples.
(cid:96) 2 -based DeepSloth.
For both the standard and universal attacks, we set the total number of iterations to 550 and the step size γ to 0 .
Our initial perturbation has the (cid:96) 2 -norm of 1 . 0 .
Here, we use the same number of samples for crafting the standard and universal perturbations as the (cid:96) ∞ -based attacks.
(cid:96) 1 -based DeepSloth.
For our standard (cid:96) 1 -based DeepSloth, we set the total number of iterations to 250 , the step size α to 0 .
5 , and the gradient sparsity to 99 .
For the universal variants, we reduce the total number of iterations to 100 and set the gradient sparsity to 90 .
Other hyperparameters remain the same.
We use the same number of samples as the (cid:96) ∞ -based attacks, to craft the perturbations.
C E MPIRICAL E VALUATION OF (cid:96) 1 AND (cid:96) 2 D EEP S LOTH
Table 4 and Table 5 shows the effectiveness of (cid:96) 1 -based
and (cid:96) 2 -based DeepSloth attacks, respectively.
[Table] [Table]
[Table] [Table]
Our results show that the (cid:96) 1 - and (cid:96) 2 -based attacks are less effective than the (cid:96) ∞ -based attacks.
In contrast to the (cid:96) ∞ -based attacks that eliminate the efﬁcacy of victim multi-exit models, the (cid:96) 1and (cid:96) 2 -based attacks reduce the efﬁcacy of the same models by 0.24 ∼ 0.65.
Besides, the accuracy drops caused by (cid:96) 1 - and (cid:96)
2 -based attacks are in 6 ∼ 21%, smaller than that of (cid:96) ∞ -based DeepSloth (75 ∼ 99%).
Moreover, we see that the universal variants of (cid:96) 1 - and (cid:96) 2 -based attacks can barely reduce the efﬁcacy of multi-exit models—they decrease the efﬁcacy up to 0 .
08 and the accuracy by 12%.
D E MPIRICAL E VALUATION OF D EEP S LOTH ON R ES N ET
Table 6 shows the the effectiveness of our DeepSloth attacks on ResNet56-base models.
[Table]
Our results show that ResNet56-based models are vulnerable to all the (cid:96) ∞ , (cid:96) 2 , and (cid:96) 1 -based DeepSloth attacks.
Using our (cid:96) ∞ -based DeepSloth, the attacker can reduce the efﬁcacy of the victim models to 0.00 ∼ 0.01 and the accuracy by 39 ∼ 68%.
Besides, the (cid:96) 2 , and (cid:96) 1 -based attacks also decrease the efﬁcacy to 0.04 ∼ 0.18 and the accuracy by 11 ∼ 44%.
Compared to the results on MSDNet, VGG16, and MobileNet in Table 4 and 5, the same attacks are more effective.
The universal variants decrease the efﬁcacy up to 0.21 and the accuracy up to 24%.
In particular, the (cid:96) 2 , and (cid:96) 1 -based attacks (on
CIFAR-10 models) are effective than the same attacks on MSDNet, VGG16, and MobileNet models.
E C OST OF C RAFTING D EEP S LOTH S AMPLES
[Table]
[Table] [Table]
In Table 7, we compare the cost of DeepSloth with other attack algorithms on a VGG16-based CIFAR10 model—executed on a single Nvidia Tesla-V
GPU.
For the universal DeepSloth, we measure the execution time for crafting a perturbation using one batch (250 samples) of the training set.
For the other attacks, we measure the time for perturbing the whole test set of CIFAR-10.
Our DeepSloth takes roughly the same time as the PGD and PGD-avg attacks and signiﬁcantly less time than the PGD-max attack.
Our universal DeepSloth takes only 2 seconds (10x faster than DeepSloth) as it only uses 250 samples.
F A DVERSARIAL E XAMPLES FROM S TANDARD A TTACKS AND D EEP S LOTH
In Figure 4, we visualize the adversarial examples from the PGD, UAP and our DeepSloth attacks.
[Figure]