Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Deep networks were recently suggested to face the odds between accuracy (on clean natural images) and robustness (on adversarially perturbed images) (Tsipras et al., 2019).
Such a dilemma is shown to be rooted in the inherently higher sample complexity (Schmidt et al., 2018) and/or model capacity (Nakkiran, 2019), for learning a high-accuracy and robust classifier.
In view of that, give a classification task, growing the model capacity appears to help draw a win-win between accuracy and robustness, yet at the expense of model size and latency, therefore posing challenges for resource-constrained applications.
Is it possible to co-design model accuracy, robustness and efficiency to achieve their triple wins?
This paper studies multi-exit networks associated with input-adaptive efficient inference, showing their strong promise in achieving a “sweet point" in co-optimizing model accuracy, robustness, and efficiency.
Our proposed solution, dubbed Robust Dynamic Inference Networks (RDI-Nets), allows for each input (either clean or adversarial) to adaptively choose one of the multiple output layers (early branches or the final one) to output its prediction.
That multi-loss adaptivity adds new variations and flexibility to adversarial attacks and defenses, on which we present a systematical investigation.
We show experimentally that by equipping existing backbones with such robust adaptive inference, the resulting RDI-Nets can achieve better accuracy and robustness, yet with over 30% computational savings, compared to the defended original models.

Deep networks, despite their high predictive accuracy, are notoriously vulnerable to adversarial attacks (Goodfellow et al., 2015; Biggio et al., 2013; Szegedy et al., 2014; Papernot et al., 2016) .
While many defense methods have been proposed to increase a model's robustness to adversarial examples, they were typically observed to hamper its accuracy on original clean images.
Tsipras et al. (2019) first pointed out the inherent tension between the goals of adversarial robustness and standard accuracy in deep networks, whose provable existence was shown in a simplified setting.
theoretically quantified the accuracy-robustness trade-off, in terms of the gap between the risk for adversarial examples versus the risk for non-adversarial examples.
It is intriguing to consider whether and why the model accuracy and robustness have to be at odds.
demonstrated that the number of samples needed to achieve adversarially robust generalization is polynomially larger than that needed for standard generalization, under the adversarial training setting.
A similar conclusion was concurred by Sun et al. (2019) in the standard training setting.
Tsipras et al. (2019) considered the accuracy-robustness trade-off as an inherent trait of the data distribution itself, indicating that this phenomenon persists even in the limit of infinite data.
Nakkiran (2019) argued from a different perspective, that the complexity (e.g. capacity) of a robust classifier must be higher than that of a standard classifier.
Therefore, replacing a largercapacity classifier might effectively alleviate the trade-off.
Overall, those existing works appear to suggest that, while accuracy and robustness are likely to trade off for a fixed classification model and on a given dataset, such trade-off might be effectively alleviated ("win-win"), if supplying more training data and/or replacing a larger-capacity classifier.
On a separate note, deep networks also face the pressing challenge to be deployed on resourceconstrained platforms due to the prosperity of smart Internet-of-Things (IoT) devices.
Many IoT applications naturally demand security and trustworthiness, e.g., , biometrics and identity verification, but can only afford limited latency, memory and energy budget.
Hereby we extend the question: can we achieve a triple-win, i.e., , an accurate and robust classfier while keeping it efficient?
This paper makes an attempt in providing a positive answer to the above question.
Rather than proposing a specific design of robust light-weight models, we reduce the average computation loads by input-adaptive routing to achieve triple-win.
To this end, we introduce the input-adaptive dynamic inference (Teerapittayanon et al., 2017; , an emerging efficient inference scheme in contrast to the (non-adaptive) model compression, to the adversarial defense field for the first time.
Given any deep network backbone (e.g., , ResNet, MobileNet), we first follow (Teerapittayanon et al., 2017) to augment it with multiple early-branch output layers in addition to the original final output.
Each input, regardless of clean or adversarial samples, adaptively chooses which output layer to take for its own prediction.
Therefore, a large portion of input inferences can be terminated early when the samples can already be inferred with high confidence.
Up to our best knowledge, no existing work studied adversarial attacks and defenses for an adaptive multi-output model, as the multiple sources of losses provide much larger flexibility to compose attacks (and therefore defenses), compared to the typical single-loss backbone.
We present a systematical exploration on how to (white-box) attack and defense our proposed multi-output network with adaptive inference, demonstrating that the composition of multiple-loss information is critical in making the attack/defense strong.
Fig.
1 illustrates our proposed Robust Dynamic Inference Networks (RDI-Nets).
We show experimentally that the input-adaptive inference and multi-loss flexibility can be our friend in achieving the desired "triple wins".
With our best defended RDI-Nets, we achieve better accuracy and robustness, yet with over 30% inference computational savings, compared to the defended original models as well as existing solutions co-designing robustness and efficiency (Gui et al., 2019; Guo et al., 2018) .
The codes will be publicly released upon acceptance.
Our proposed RDI-Net framework, a defended multi-output network enabling dynamic inference.
Each image, being it clean or adversarially perturbed, adaptively picks one branch to exit.
Intuition: Multi-Output Networks as Special Ensembles Our intuition on defending multioutput networks arises from the success of ensemble defense in improving both accuracy and robustness (Tramèr et al., 2018; Strauss et al., 2017) , which also aligns with the model capacity hypothesis (Nakkiran, 2019) .
A general multi-output network could be decomposed by an ensemble of single-output models, with weight re-using enforced among them.
It is thus more compact than an ensemble of independent models, and the extent of sharing weight calibrates ensemble diversity versus efficiency.
Therefore, we expect a defended multi-output network to (mostly) inherit the strong accuracy/robustness of ensemble defense, while keeping the inference cost lower.
Do "Triple Wins" Go Against the Model Capacity Needs?
We point out that our seemingly "free" efficiency gains (e.g., not sacrificing TA/ATA) do not go against the current belief that a more accurate and robust classifier relies on a larger model capacity (Nakkiran, 2019) .
From the visualization, there remains to be a portion of clean/adversarial examples that have to utilize the full inference to predict well.
In other words, the full model capacity is still necessary to achieve our current TAs/ATAs.
Meanwhile, just like in standard classification , not all adversarial examples are born equally.
Many of them can be predicted using fewer inference costs (taking earlier exits).
Therefore, RDI-Nets reduces the "effective model capacity" averaged on all testing samples for overall higher inference efficiency, while not altering the full model capacity.
This paper targets to simultaneously achieve high accuracy and robustness and meanwhile keeping inference costs lower.
We introduce the multi-output network and input-adaptive dynamic inference, as a strong tool to the adversarial defense field for the first time.
Our RDI-Nets achieve the "triple wins" of better accuracy, stronger robustness, and around 30% inference computational savings.
Our future work will extend RDI-Nets to more dynamic inference mechanisms, e.g., .
Is it possible to co-design model accuracy, robustness and efficiency to achieve their triple wins? Yes!