B ENCHMARKING N EURAL N ETWORK R OBUSTNESS TO C OMMON C ORRUPTIONS AND P ERTURBATIONS
Anonymous authors
Paper under double-blind review
A BSTRACT
In this paper we establish rigorous benchmarks for image classiﬁer robustness.
Our ﬁrst benchmark, I MAGE N ET -C, standardizes and expands the corruption robustness topic, while showing which classiﬁers are preferable in safety-critical applications.
Then we propose a new dataset called I MAGE N ET -P which enables researchers to benchmark a classiﬁer’s robustness to common perturbations.
Unlike recent robustness research, this benchmark evaluates performance on common corruptions and perturbations not worst-case adversarial perturbations.
We ﬁnd that there are negligible changes in relative corruption robustness from AlexNet classiﬁers to ResNet classiﬁers.
Afterward we discover ways to enhance corruption and perturbation robustness.
We even ﬁnd that a bypassed adversarial defense provides substantial common perturbation robustness.
Together our benchmarks may aid future work toward networks that robustly generalize.
I NTRODUCTION
The human vision system is robust in ways that existing computer vision systems are not (Recht et al., 2018; Azulay & Weiss, 2018).
Unlike current deep learning classiﬁers (Krizhevsky et al., 2012;
He et al., 2015; Xie et al., 2016), the human vision system is not fooled by small changes in query images.
Humans are also not confused by many forms of corruption such as snow, blur, pixelation, and novel combinations of these.
Humans can even deal with abstract changes in structure and style.
Achieving these kinds of robustness is an important goal for computer vision and machine learning.
It is also essential for creating deep learning systems that can be deployed in safety-critical applications.
Most work on robustness in deep learning methods for vision has focused on the important challenges of robustness to adversarial examples (Szegedy et al., 2014; Carlini & Wagner, 2017; 2016), unknown unknowns (Liu et al., 2018), and model or data poisoning (Blanchard et al., 2017;
Steinhardt et al., 2017; Hendrycks et al., 2018).
In contrast, we develop and validate datasets for two other forms of robustness.
Speciﬁcally, we introduce the I MAGET N ET -C dataset for input corruption robustness (Vasiljevic et al., 2016) and the I MAGE N ET -P dataset for perturbation robustness .
To create I MAGE N ET -C, we introduce a set of 75 common visual corruptions and apply them to the
ImageNet object recognition challenge (Deng et al., 2009).
We hope that this will serve as a general dataset for benchmarking robustness to image corruptions and prevent methodological problems such as moving goal posts and result cherry picking.
We evaluate the performance of current deep learning systems and show that there is wide room for improvement on I MAGE N ET -C.
We also introduce a total of three methods and architectures that improve corruption robustness without losing accuracy.
To create I MAGE N ET -P, we introduce a set of perturbed or subtly differing ImageNet images.
Using metrics we propose, we measure the stability of the network’s predictions on these perturbed images.
Although these perturbations are not chosen by an adversary, currently existing networks exhibit surprising instability on common perturbations.
Then we then demonstrate that approaches which enhance corruption robustness can also improve perturbation robustness.
For example, some recent architectures can greatly improve both types of robustness.
More, we show that the Adversarial
Logit Pairing (cid:96) ∞ adversarial example defense can yield substantial robustness gains on diverse and common perturbations.
By deﬁning and benchmarking perturbation and corruption robustness, we facilitate research that can be overcome by future networks which do not rely on spurious correlations or cues inessential to the object’s class.
R ELATED W ORK
Adversarial Examples.
An adversarial image is a clean image perturbed by a small distortion carefully crafted to confuse a classiﬁer.
These deceptive distortions can occasionally fool black-box classiﬁers (Kurakin et al., 2017).
Algorithms have been developed that search for the smallest additive distortions in RGB space that are sufﬁcient to confuse a classiﬁer (Carlini et al., 2017).
Thus adversarial distortions serve as type of worst-case analysis for network robustness.
Its popularity has often led “adversarial robustness” to become interchangeable with “robustness” in the literature (Bastani et al., 2016; Rauber et al., 2017).
In the literature, new defenses (Lu et al., 2017; Papernot et al., 2017; Metzen et al., 2017; Hendrycks & Gimpel, 2017) often quickly succumb to new attacks (Evtimov et al., 2017; Carlini & Wagner, 2017; 2016), with some exceptions for perturbations on small images (Schott et al., 2018; Madry et al., 2018; Sharma & Chen, 2018).
For some simple datasets, the existence of any classiﬁcation error ensures the existence of adversarial perturbations of size O
( d − 1 / 2 ) , d the input dimensionality (Gilmer et al., 2018b).
For some simple models, adversarial robustness requires an increase in the training set size that is polynomial in d (Schmidt et al., 2018).
Gilmer et al.
(2018a) suggest modifying the problem of adversarial robustness itself for increased real-world applicability.
Robustness in Speech.
Speech recognition research emphasizes robustness to common corruptions rather than worst-case, adversarial corruptions (Li et al., 2014; Mitra et al., 2017).
Common acoustic corruptions (e.g., street noise, background chatter, wind) receive greater focus than adversarial audio, because common corruptions are ever-present and unsolved.
There are several popular datasets containing noisy test audio (Hirsch & Pearce, 2000; Hirsch, 2007).
Robustness in noisy environments requires robust architectures, and some research ﬁnds convolutional networks more robust than fully connected networks (Abdel-Hamid et al., 2013).
Additional robustness has been achieved through pre-processing techniques such as standardizing the statistics of the input (Liu et al., 1993; Torre et al., 2005; Harvilla & Stern, 2012; Kim & Stern, 2016).
ConvNet Fragility Studies.
Several studies demonstrate the fragility of convolutional networks on simple corruptions.
For example, Hosseini et al.
(2017) apply impulse noise to break Google’s
Cloud Vision API.
Using Gaussian noise and blur, Dodge & Karam (2017b) demonstrate the superior robustness of human vision to convolutional networks, even after networks are ﬁne-tuned on Gaussian noise or blur.
Geirhos et al.
(2017) compare networks to humans on noisy and elastically deformed images.
They ﬁnd that ﬁne-tuning on speciﬁc corruptions does not generalize and that classiﬁcation error patterns underlying network and human predictions are not similar.
Temel et al.
Temel & AlRegib (2018) propose corrupted datasets for trafﬁc sign recognition, and shortly after our public draft, Temel et al.
(2018) propose a different controlled-environment dataset with seven corruptions.
Robustness Enhancements.
In an effort to reduce classiﬁer fragility, Vasiljevic et al.
(2016) ﬁnetune on blurred images.
They ﬁnd it is not enough to ﬁne-tune on one type of blur to generalize to other blurs.
Furthermore, ﬁne-tuning on several blurs can marginally decrease performance.
Zheng et al.
(2016) also ﬁnd that ﬁne-tuning on noisy images can cause underﬁtting, so they encourage the noisy image softmax distribution to match the clean image softmax.
Dodge & Karam (2017a) address underﬁtting via an ensemble.
They ﬁne-tune each network on one corruption and classify with an mixture of these corruption-speciﬁc experts, though they do not assess performance on combinations of known corruptions.
C ORRUPTIONS , P ERTURBATIONS , AND A DVERSARIAL P ERTURBATIONS
We now deﬁne corruption and perturbation robustness and distinguish them from adversarial perturbation robustness.
To begin, we consider a classiﬁer f : X → Y trained on samples from distribution D , a set of corruption functions C , and a set of perturbation functions E .
We let P
C ( c ) , P E ( ε ) approximate the real-world frequency of these corruptions and perturbations.
Most classiﬁers are judged by their accuracy on test queries drawn from D , i.e., P ( x,y )
∼D ( f ( x ) =
y ) .
Yet in a vast range of cases the classiﬁer is tasked with classifying low-quality or corrupted inputs.
In view of this, we suggest also computing the classiﬁer’s corruption robustness E c ∼ C
[ P ( x,y ) ∼D ( f ( c ( x ) = y ))] .
This contrasts with a popular notion of adversarial robustness, often formulated min (cid:107) δ (cid:107) p <
b P ( x,y ) ∼D ( f ( x + δ ) =
y ) , b a small budget.
Thus, corruption robustness measures the classiﬁer’s average-case performance on corruptions C , while adversarial robustness measures the worst-case performance on small, additive, classiﬁer-tailored perturbations.
Gaussian Noise
Shot Noise
Impulse Noise
Defocus Blur Frosted Glass Blur
Motion Blur
Zoom Blur
Snow
Frost
Fog
Brightness
Contrast
Elastic
Pixelate
JPEG
Average-case performance on small, general, classiﬁer-agnostic perturbations motivates us to deﬁne perturbation robustness , namely E ε ∼E
[ P ( x,y ) ∼D ( f ( ε ( x ))
=
f ( x ))] .
Consequently, in measuring perturbation robustness, we track the classiﬁer’s prediction stability, reliability, or consistency in the face of minor input changes.
Now in order to approximate C, E and these robustness measures, we designed a set of corruptions and perturbations which are frequently encountered in natural images.
We will refer to these as “common” corruptions and perturbations.
These common corruptions and perturbations are available in the form of I MAGE N ET -C and I MAGE N ET -P.
T HE I MAGE N ET -C AND I MAGE N ET -P R OBUSTNESS B ENCHMARKS 4. T HE D ATA OF I MAGE N ET -C AND I MAGE N ET -P
I MAGE N ET -C Design.
The I MAGE N ET -C benchmark consists of 15 diverse corruption types applied to validation images of ImageNet.
The corruptions are drawn from four main categories— noise, blur, weather, and digital—as shown in Figure 1.
Research that improves performance on this benchmark should indicate general robustness gains, as the corruptions are diverse and numerous.
Each corruption type has ﬁve levels of severity since corruptions can manifest themselves at varying intensities.
Appendix A gives an example of the ﬁve different severity levels for impulse noise.
Real-world corruptions also have variation even at a ﬁxed intensity.
To simulate these, we introduce variation for each corruption when possible.
For example, each fog cloud is unique to each image.
These algorithmically generated corruptions are applied to the ImageNet (Deng et al., 2009) validation images to produce our corruption robustness dataset I MAGE N ET -C.
The dataset can be downloaded or re-created by visiting [anonymized] .
Our benchmark tests networks with I MAGE N ET -C images, but networks should not be trained on these images .
Networks should be trained on datasets such as ImageNet and not be trained on I MAGE N ET -C corruptions.
To enable further experimentation, we designed an extra corruption type for each corruption category (Appendix B).
In addition, we provide versions of CIFAR-10-C, T INY I MAGE N ET -C, I MAGE N ET 64 × 64 -C, and I MAGE N ET -C with image sizes suitable for Inception classiﬁers.
Overall, the I MAGE N ET -C dataset consists ofcorruptions, all applied to ImageNet validation images for testing a pre-existing network.
Common Corruptions.
The ﬁrst corruption type is Gaussian noise .
This corruption can appear in low-lighting conditions.
Shot noise , also called Poisson noise, is electronic noise caused by the discrete nature of light itself.
Impulse noise is a color analogue of salt-and-pepper noise and can be caused by bit errors.
Defocus blur occurs when an image is out of focus.
Frosted Glass Blur appears with “frosted glass” windows or panels.
Motion blur appears when a camera is moving quickly.
Zoom blur occurs when a camera moves toward an object rapidly.
Snow is a visually obstructive form of precipitation.
Frost forms when lenses or windows are coated with ice crystals.
Fog shrouds objects and is rendered with the diamond-square algorithm.
Brightness varies with daylight intensity.
Contrast can be high or low depending on lighting conditions and the photographed object’s color.
Elastic transformations stretch or contract small image regions.
Pixelation occurs when upsampling a low-resolution image.
JPEG is a lossy image compression format that increases image pixelation and introduces artifacts.
By providing ﬁve levels of severity for each corruption type, we can test architectures for robustness at depth.
The broad range of different corruption types allows architectures which are used in various applications to be tested for robustness at breadth.
T =
Tilt
T =
T = T =
Brightness
I MAGE N ET -P Design.
The second benchmark that we propose tests the classiﬁer’s perturbation robustness.
Models lacking in perturbation robustness produce erratic predictions which undermines user trust.
When perturbations have a high propensity to change the model’s response, then perturbations could also misdirect or destabilize iterative image optimization procedures appearing in style transfer (Gatys et al., 2016), decision explanations (Fong & Vedaldi, 2017), feature visualization (Olah et al., 2017), and so on.
Like I MAGE N ET -C, I MAGE N ETP consists of noise, blur, weather, and digital distortions.
Also as before, the dataset has validation perturbations; has difﬁculty levels; has CIFAR-10, Tiny ImageNet, ImageNet 64 × 64 , standard, and Inception-sized editions; and has been designed for benchmarking not training networks.
[Equation]
departs from I MAGE N ET -C by having perturbation sequences generated from each ImageNet validation image; examples are in Figure 2.
Each sequence contains more than 30 frames, so we counteract an increase in dataset size and evaluation time by using only 10 common perturbations.
Common Perturbations.
Appearing more subtly than the corruption from I MAGE N ET -C, the Gaussian noise perturbation sequence begins with the clean ImageNet image.
The following frames in the sequence consist in the same image but with minute Gaussian noise perturbations applied.
This sequence design is similar for the shot noise perturbation sequence.
However the remaining perturbation sequences have temporality, so that each frame of the sequence is a perturbation of the previous frame.
Since each perturbation is small, repeated application of a perturbation does not bring the image far out-of-distribution.
For example, an I MAGE N ET -P translation perturbation sequence shows a clean ImageNet image sliding from right to left one pixel at a time; with each perturbation of the pixel locations, the resulting frame is still of high quality.
The perturbation sequences with temporality are created with motion blur, zoom blur, snow, brightness, translate, rotate, tilt (viewpoint variation through minor 3D rotations), and scale perturbations.
4. I MAGE N ET -C AND I MAGE N ET -P M ETRICS AND S ETUP
I MAGE N ET -C Metrics.
Common corruptions such as Gaussian noise can be benign or destructive depending on their severity.
In order to comprehensively evaluate a classiﬁer’s robustness to a given type of corruption, we score the classiﬁer’s performance across ﬁve corruption severity levels and aggregate these scores.
The ﬁrst evaluation step is to take a trained classiﬁer f, which has not been trained on I MAGE N ET -C, and compute the clean dataset top-1 error rate.
Denote this error rate
E f clean .
The second step is to test the classiﬁer on each corruption type c at each level of severity s ( 1 ≤ s ≤ 5 ).
This top-1 error is written E fs,c .
Before we aggregate the classiﬁer’s performance across severities and corruption types, we will make error rates more comparable since different corruptions pose different levels of difﬁculty.
For example, fog corruptions often obscure an object’s class more than brightness corruptions.
We adjust for the varying difﬁculties by dividing by AlexNet’s errors, but any baseline network will do.
This standardized aggregate performance measure is the Corruption
Error, computed with the formula
[Equation]
Now we can summarize model corruption robustness by averaging the 15 Corruption Error values
CE f Gaussian Noise , CE f Shot Noise , . .
. , CE f JPEG .
This results in the mean CE or mCE for short.
We now introduce a more nuanced corruption robustness measure.
Consider a classiﬁer that withstands most corruptions, so that the gap between the mCE and the clean data error is minuscule.
Contrast this with a classiﬁer with a low clean error rate which has its error rate spike in the presence of corruptions; this corresponds to a large gap between the mCE and clean data error.
It is possible that the former classiﬁer has a larger mCE than the latter, despite the former degrading more gracefully in the presence of corruptions.
The amount that the classiﬁer declines on corrupted inputs is given by the formula
[Equation]
E
[Equation]
Averaging these 15 Relative Corruption Errors results in the Relative mCE .
This measures the relative robustness or the performance degradation when encountering corruptions.
I MAGE N ET -P Metrics.
A straightforward approach to estimate E ε ∼E [ P ( x,y ) ∼D
( f ( ε ( x ))
(cid:54) =
f ( x ))] falls into place when using I MAGE N ET -P perturbation sequences.
Let us denote m perturbation sequences with S = (cid:8)(cid:0)
x
( i ) 1 , x ( i ) 2 , . . .
, x ( i ) n (cid:1)(cid:9) m i =1 where each sequence is made with perturbation p .
The “Flip Probability” of network f : X → { 1 , 2 , . . . , 1000 } on perturbation sequences S is
[Equation]
For noise perturbation sequences, which are not temporally related, x ( i )is clean and x ( i ) j
( j > 1) are perturbed images of x ( i ) 1 .
We can recast the FP formula for noise sequences as
FP fp =
[Equation]
j
[Equation]
(cid:54) =
f
[Equation]
x
[Equation]
) 1 (cid:1)(cid:1) =
P x ∼S ( f ( x j ) (cid:54) =
f ( x 1 ) | j > 1) .
As was done with the Corruption Error formula, we now standardize the Flip Probability by the sequence’s difﬁculty for increased commensurability.
We have, then, the “Flip Rate” FR fp = FP fp / FP AlexNet p .
Averaging the Flip Rate across all perturbations yields the mean Flip Rate or mFR .
We do not deﬁne a “relative mFR” since we did not ﬁnd any natural formulation, nor do we directly use predicted class probabilities due to differences in model calibration (Guo et al., 2017).
When the top-5 predictions are relevant, perturbations should not cause the list of top-5 predictions to shufﬂe chaotically, nor should classes sporadically vanish from the list.
We penalize topinconsistency of this kind with a different measure.
Let the ranked predictions of network f on x be the permutation τ ( x ) ∈ S 1000 .
Concretely, if “Toucan” has the label 97 in the output space and “Pelican” has the label 145, and if f on x predicts “Toucan” and “Pelican” to be the most and second-most likely classes, respectively, then τ ( x )(97)
= 1 and τ ( x )(144) = 2 .
These permutations contain the top-5 predictions, so we use permutations to compare top-5 lists.
To do this, we deﬁne
[Equation]
where σ = ( τ ( x )) − 1 τ ( x (cid:48) ) .
If the top-5 predictions represented within τ ( x ) and τ ( x (cid:48) ) are identical, then d ( τ ( x ) , τ ( x (cid:48) ))
= 0 .
More examples of d on several permutations are in Appendix C. Comparing the top-5 predictions across entire perturbation sequences results in the unstandardized TopDistance uT5D
[Equation]
τ ( x j ) , τ ( x j − 1 )) .
For noise perturbation sequences, we have uT5D fp = E x ∼S [ d ( τ ( x j ) , τ ( x 1 ))
| j > 1] .
Once the uT5D is standardized, we have the Top-5 Distance T5D fp =
uT5D fp / uT5D AlexNet p .
The T5Ds averaged together correspond to the mean Top-5 Distance or mT5D .
[Figure]
[Figure]
Preserving Metric Validity.
The goal of I MAGE N ET -C and I MAGE N ET -P is to evaluate the robustness of machine learning algorithms on novel corruptions and perturbations.
Humans are able to generalize to novel corruptions quite well; for example, they can easily deal with new Instagram ﬁlters.
Likewise for perturbations; humans relaxing in front of an undulating ocean do not give turbulent accounts of the scenery before them.
Hence, we propose the following protocol.
The image recognition network should be trained on the ImageNet training set and on whatever other training sets the investigator wishes to include.
However, the network should not be trained on any of the I MAGE N ET -C corruptions or I MAGE N ET -P perturbations, as this does not generalize (see Section 2).
We allow training with other distortions (e.g., uniform noise) and standard data augmentation (i.e., cropping, mirroring), even though cropping overlaps with translations.
Then the resulting trained model should be evaluated on
I MAGE N ET -C or I MAGE N ET -P using the above metrics.
Optionally, researchers can test with the separate set of validation corruptions and perturbations we provide for I MAGE N ET -C and I MAGE N ET -P.
E XPERIMENTS 5. A RCHITECTURE R OBUSTNESS
How robust are current methods, and has progress in computer vision been achieved at the expense of robustness?
As seen in Figure 3, as architectures improve, so too does the mean Corruption Error (mCE).
By this measure, architectures have become progressively more successful at generalizing to corrupted distributions.
Note that models with similar clean error rates have fairly similar CEs, and in
Table 1 there are no large shifts in a corruption type’s CE.
Consequently, it would seem that architectures have slowly and consistently improved their representations over time.
However, it appears that corruption robustness improvements are mostly explained by accuracy improvements.
Recall that the
Relative mCE tracks a classiﬁer’s accuracy decline in the presence of corruptions.
Figure 3 shows that the Relative mCEs of many subsequent models are worse than that of AlexNet (Krizhevsky et al., 2012).
Full results are in Appendix D. In consequence, from AlexNet to ResNet (He et al., 2015), corruption robustness in itself has barely changed.
For these architectures, relative corruption robustness remains near AlexNet-levels and therefore below human-level, which shows that our “superhuman” classiﬁers are decidedly subhuman.
On perturbed inputs, current classiﬁers are unexpectedly bad.
For example, a ResNet-18 on Scale perturbation sequences have a 15.6% probability of ﬂipping its top-1 prediction between adjacent frames (i.e., FP ResNet-18 Scale = 15 . 6% ); the uT5D ResNet-18 Scale is 3 . 6 .
More results are in
Appendix E. Clearly perturbations need not be adversarial to fool current classiﬁers.
What is also surprising is that while
VGGNets are worse than ResNets at generalizing to corrupted examples, on perturbed examples they can be just as robust or even more robust.
Likewise, Batch Normalization made VGG-19 less robust to perturbations but more robust to corruptions.
Yet this is not to suggest that there is a fundamental trade-off between corruption and perturbation robustness.
In fact, both corruption and perturbation robustness can improve together, as we shall see later.
[Table]
5. R OBUSTNESS E NHANCEMENTS
Be aware that Appendix F contains many informative failures in robustness enhancement.
Those experiments underscore the necessity in testing on a a diverse test set, the difﬁculty in cleansing corruptions from image, and the futility in expecting robustness gains from some “simpler” models.
Histogram Equalization.
Histogram equalization successfully standardizes speech data for robust speech recognition (Torre et al., 2005; Harvilla & Stern, 2012).
For images, we ﬁnd that preprocessing with Contrast Limited Adaptive Histogram Equalization (Pizer et al., 1987) is quite effective.
Unlike our image denoising attempt (Appendix F), CLAHE reduces the effect of some corruptions while not worsening performance on most others, thereby improving the mCE.
We demonstrate CLAHE’s net improvement by taking a pre-trained ResNet-50 and ﬁne-tuning the whole model for ﬁve epochs on images processed with CLAHE.
The ResNet-50 has a 23.87% error rate, but ResNet-50 with CLAHE has an error rate of 23.55%.
On nearly all corruptions, CLAHE slightly decreases the Corruption
Error.
The ResNet-50 without CLAHE preprocessing has an mCE of 76.7%, while with CLAHE the
ResNet-50’s mCE decreases to 74.5%.
Multiscale Networks.
Multiscale architectures achieve greater corruption robustness by propagating features across scales at each layer rather than slowly gaining a global representation of the input as in typical convolutional neural networks.
Some multiscale architectures are called
Multigrid Networks (Ke et al., 2017).
Multigrid networks each have a pyramid of grids in each layer which enables the subsequent layer to operate across scales.
Along similar lines, Multi-Scale Dense
Networks (MSDNets) (Huang et al., 2018) use information across scales.
MSDNets bind network layers with DenseNet-like (Huang et al., 2017b) skip connections.
These two different multiscale networks both enhance corruption robustness, but they do not provide any noticeable beneﬁt in perturbation robustness.
Now before comparing mCE values, we ﬁrst note the Multigrid network has a 24.6% top-1 error rate, as does the MSDNet, while the ResNet-50 has a 23.9% top-1 error rate.
On noisy inputs, Multigrid networks noticeably surpass ResNets and MSDNets, as shown in
Figure 5.
Since multiscale architectures have high-level representations processed in tandem with ﬁne details, the architectures appear better equipped to suppress otherwise distracting pixel noise.
When all corruptions are evaluated, ResNet-50 has an mCE of 76.7%, the MSDNet has an mCE of 73.6%, and the Multigrid network has an mCE of 73.3%.
Feature Aggregating and Larger Networks.
Some recent models enhance the ResNet architecture by increasing what is called feature aggregation.
Of these, DenseNets and ResNeXts (Xie et al., 2016) are most prominent.
Each purports to have stronger representations than ResNets, and the evidence is largely a hard-won ImageNet error-rate downtick.
Interestingly, the I MAGE N ET -C mCE clearly indicates that DenseNets and ResNeXts have superior representations.
Accordingly, a switch from a ResNet-50 (23.9% top-1 error) to a DenseNet-121 (25.6% error) decreases the mCE from 76.7% to 73.4% (and the relative mCE from 105.0% to 92.8%).
More starkly, switching from a
ResNet-50 to a ResNeXt-50 (22.9% top-1) drops the mCE from 76.7% to 68.2% (relative mCE decreases from 105.0% to 88.6%).
Corruption robustness results are summarized in Figure 5.
This shows that corruption robustness may be a better way to measure future progress in representation learning than the clean dataset top-1 error rate.
Some of the greatest and simplest robustness gains sometimes emerge from making recent models more monolithic.
Apparently more representations, more redundancy, and more capacity allow these massive models to operate more stably on corrupted inputs.
We saw earlier that making models smaller
[Figure] [Figure]
achieve robustness gains that substantially outpace their accuracy gains.
does the opposite.
Swapping a DenseNet-121 (25.6% top-1) with the larger DenseNet-161 (22.9% top1) decreases the mCE from 73.4% to 66.4% (and the relative mCE from 92.8% to 84.6%).
In a similar fashion, a ResNeXt-50 (22.9% top-1) is less robust than the a giant ResNeXt-101 (21.0% top-1).
The mCEs are 68.2% and 62.2% respectively (and the relative mCEs are 88.6% and 80.1% respectively).
Both model size and feature aggregation results are summarized in Figure 6.
Consequently, future models with even more depth, width, and feature aggregation may attain further corruption robustness.
Feature aggregation and their larger counterparts similarly improve perturbation robustness.
While a
ResNet-50 has a 58.0% mFR and a 78.3% mT5D, a DenseNet-121 obtains a 56.4% mFR and 76.8% mT5D, and a ResNeXt-50 does even better with a 52.4% mFR and a 74.2% mT5D.
Reﬂecting the corruption robustness ﬁndings further, the larger DenseNet-161 has a 46.9% mFR and 69.5% mT5D, while the ResNeXt-101 has a 43.2% mFR and 65.9% mT5D.
Thus in two senses feature aggregating networks and their larger versions markedly enhance robustness.
Adversarial Logit Pairing.
ALP is an adversarial example defense for large-scale image classiﬁers (Kannan et al., 2018).
Like nearly all other adversarial defenses, ALP was bypassed and has unclear value as an adversarial defense going forward (Engstrom et al., 2018), yet this is not a decisive reason dismiss it.
ALP provides signiﬁcant perturbation robustness even though it does not provide much adversarial perturbation robustness against all adversaries.
Although ALP was designed to increase robustness to small gradient perturbations, it markedly improves robustness to all sorts of noise, blur, weather, and digital I MAGE N ET -P perturbations—methods generalizing this well is a rarity.
In point of fact, a publicly available Tiny ImageNet ResNet-50 model ﬁne-tuned with ALP has a 41% and 40% relative decrease in the mFP and mT5D on T INY I MAGE N ET -P, respectively.
ALP’s immense success in enhancing common perturbation robustness and its modest utility for adversarial perturbation robustness highlights that the interplay between these problems should be better understood.
C ONCLUSION
In this paper, we introduced what are to our knowledge the ﬁrst comprehensive benchmarks for corruption and perturbation robustness.
This was made possible by introducing two new datasets,
I MAGE N ET -C and I MAGE N ET -P.
The ﬁrst of which showed that many years of architectural advancements corresponded to minuscule changes in relative corruption robustness.
Therefore benchmarking and improving robustness deserves attention, especially as top-1 clean ImageNet accuracy nears its ceiling.
We also saw that classiﬁers exhibit unexpected instability on simple perturbations.
Thereafter we found that methods such as histogram equalization, multiscale architectures, and larger featureaggregating models improve corruption robustness.
These larger models also improve perturbation robustness.
However, we found that even greater perturbation robustness can come from an adversarial defense designed for adversarial (cid:96) ∞ perturbations, indicating a surprising interaction between adversarial and common perturbation robustness.
In this work, we found several methods to increase robustness, introduced novel experiments and metrics, and created new datasets for the rigorous study of model robustness, a pressing necessity as models are unleashed into safety-critical real-world settings.
E XAMPLE OF I MAGE N
ET
[Figure]
C
S
EVERITIES
Clean
Severity = Severity =
Severity = Severity =
Severity =
In Figure 7, we show the Impulse noise corruption type in ﬁve different severities.
Clearly, I MAGE N ETC corruptions can range from negligible to pulverizing.
Because of this range, the benchmark comprehensively assesses each corruption type.
B E XTRA I MAGE N ET -C C ORRUPTIONS
[Figure]
Gaussian
[Figure]
Spatter
Saturate
Directly ﬁtting the types of I MAGE N ET -C corruptions should be avoided, as it would cause researchers to overestimate a model’s robustness.
Therefore, it is incumbent on us to simplify model validation.
This is why we provide an additional form of corruption for each of the four general types.
These are available for download at [anonymized] .There is one corruption type for each noise, blur, weather, and digital category in the validation set.
The ﬁrst corruption type is speckle noise , an additive noise where the noise added to a pixel tends to be larger if the original pixel intensity is larger.
Gaussian blur is a low-pass ﬁlter where a blurred pixel is a result of a weighted average of its neighbors, and farther pixels have decreasing weight in this average.
Spatter can occlude a lens in the form of rain or mud.
Finally, saturate is common in edited images where images are made more or less colorful.
See Figure 8 for instances of each corruption type.
C M ORE ON THE I MAGE N ET -P M ETRICS AND S ETUP
For some readers, the following function may be opaque,
[Equation]
where σ = ( τ ( x )) − 1 τ ( x (cid:48) ) and the empty sum is understood to be zero.
A high-level view of d is that it computes the deviation between the top-5 predictions of two prediction lists.
For simplicity we ﬁnd the deviation between the identity and σ rather than τ ( x ) and τ ( x (cid:48) ) .
In consequence we can consider d (cid:48) ( σ ) :
= d ( 1 , σ ) where 1 the identity permutation.
To give some intuition, we provide concrete examples of d (cid:48) on permutations.
If σ is the identity, then d (cid:48) ( σ ) = 0 .
If σ = (1 , 2 , 3 , 4 , 6 , 5 , 7 , 8 , . . . )
, d (cid:48) ( σ ) = 1 .
If σ = (1 , 2 , 3 , 4 , 6 , 7 , 5 , 8 , . . . )
, d (cid:48) ( σ ) = 1 .
Once 5 fell out of the top-5, its displacement did not accumulate any further; this may happen when only the top-5 predictions are shown to the user.
If σ = (2 , 1 , 3 , 4 , 5 , 6 , . . . )
, d (cid:48) ( σ ) = 2 .
If σ = (3 , 1 , 2 , 4 , 5 , 6 , . . . )
, d (cid:48) ( σ ) = 4 .
Also, d (cid:48) ((2 , 3 , 4 , 5 , 6 , . . . , 1))
= 5 .
Distinctly, d (cid:48) ((1 , 2 , 3 , 5 , 6 , 4 , 7 , 8 , . . . ))
= 2 .
As a ﬁnal example, d (cid:48) ((5 , 4 , 3 , 2 , 1 , 6 , 7 , 8 , 9 , . . . ))
= 12 .
It may be that we want perturbation robustness for all predictions, including classes with lesser relevance.
In such cases, it is still common that the displacement of the top prediction matters more than the displacement of, say, the 500th ranked class.
For this there are many possibilities, such as the measure d (cid:48)
( σ ) = (cid:80) 1000 i =
w i | w i − w σ ( i ) | such that w i = 1 /i .
This uses a Zipﬁan assumption about the rankings of the classes: the ﬁrst class is n times as relevant as the n th class.
Other possibilities involve using logarithms rather than hyperbolic functions as in the discounted cumulative gain (Kumar &
Vassilvitskii, 2010).
One could also use the class probabilities provided by the model (should they exist).
However such a measure could make it difﬁcult to compare models since some models tend to be more uncalibrated than others (Guo et al., 2017).
As progress is made on this task, researchers may be interested in perturbations which are more likely to cause unstable predictions.
To accomplish that, researchers can simply compare a frame with the frame two frames ahead rather than just one frame ahead.
We provide concrete code of this slight change in the metric at [anonymized] .
For nontemporal perturbation sequences, i.e., noise sequences, we provide sequences where the noise perturbation is larger.
D F ULL C ORRUPTION R OBUSTNESS R ESULTS
I MAGE N ET -C corruption relative robustness results are in Table 2.
Since we use AlexNet errors to normalize Corruption Error values, we now specify the value 15 (cid:80) 5 s =1 E AlexNet s, Corruption for each corruption type.
Gaussian Noise: 88.6%, Shot Noise: 89.4%, Impulse Noise: 92.3%, Defocus Blur: 82.0%, Glass Blur: 82.6%, Motion Blur: 78.6%, Zoom Blur: 79.8%, Snow: 86.7%, Frost: 82.7%, Fog: 81.9%, Brightness: 56.5%, Contrast: 85.3%, Elastic Transformation: 64.6%,
Pixelate: 71.8%, JPEG: 60.7%, Speckle Noise: 84.5%, Gaussian Blur: 78.7%, Spatter: 71.8%,
Saturate:
[Table] [Table]
E F ULL P ERTURBATION R OBUSTNESS R ESULTS
I MAGE N ET -P mFR values are in Table 3, and mT5D values are in Table 4.
Since we use AlexNet errors to normalize our measures, we now specify the value FP AlexNet Perturbation for each corruption type.
Gaussian Noise: 23.65%, Shot Noise: 30.06%, Motion Blur: 9.30%, Zoom Blur: 5.94%,
Snow: 11.93%, Brightness: 4.89%, Translate: 11.01%, Rotate: 13.10%, Tilt: 7.05%, Scale: 23.53%, Speckle Noise: 18.65%, Gaussian Blur: 2.78%, Spatter: 5.05%, Shear: 10.66%.
Also, the uT5D AlexNetPerturbation values are as follows.
Gaussian Noise: 4.77, Shot Noise: 5.76, Motion
Blur: 1.93, Zoom Blur: 1.34, Snow: 2.42, Brightness: 1.19, Translate: 2.63, Rotate: 2.95,
[Table]
[Table]
F I NFORMATIVE R OBUSTNESS E NHANCEMENT A TTEMPTS
Stability Training.
Stability training is a technique to improve the robustness of deep networks (Zheng et al., 2016).
The method’s creators found that training on images corrupted with noise can lead to underﬁtting, so they instead propose minimizing the cross-entropy from the noisy image’s softmax distribution to the softmax of the clean image.
The authors evaluated performance on images with subtle differences and suggested that the method provides additional robustness to
JPEG corruptions.
We ﬁne-tune a ResNet-50 with stability training for ﬁve epochs.
For training with noisy images, we corrupt images with uniform noise, where the maximum and minimum of the uniform noise is tuned over { 0 . 01 , 0 .
05 , 0 . 1 } , and the stability weight is tuned over { 0 . 01 , 0 . 05 , 0 . 1 } .
Across all noise strengths and stability weight combinations, the models with stability training tested on I MAGE N ET -C have a larger mCEs than the baseline ResNet-50’s mCE.
Even on unseen noise corruptions, stability training does not increase robustness.
However, the perturbation robustness slightly improves.
The best model according to the I MAGE N ET -P validation set has an mFR of 57% , while the original ResNet’s mFR is 58% .
An upshot of this failure is that benchmarking robustness-enhancing techniques requires a diverse test set.
Image Denoising.
An approach orthogonal to modifying model representations is to improve the inputs using image restoration techniques.
Although general image restoration techniques are not yet mature, denoising restoration techniques are not.
We thus attempt restore an image with the denoising technique called non-local means (Buades & Coll, 2005).
The amount of denoising applied is determined by the noise estimation technique of Donoho & Johnstone (1993).
Therefore clean images receive should nearly no modiﬁcations from the restoration method, while noisy images should undergo considerable restoration.
We found that denoising increased the mCE from 76 .
7% to 82 .
A plausible account is that the non-local means algorithm striped the images of their subtle details even when images lacked noise, despite having the non-local means algorithm governed by the noise estimate.
Therefore, the gains in noise robustness were wiped away by subtle blurs to images with other types of corruptions, showing that targeted image restoration can prove harmful for robustness.
10-Crop Classiﬁcation.
Viewing an object at several different locations may give way to a more stable prediction.
Having this intuition in mind, we perform 10-crop classiﬁcation.
10-crop classiﬁcation is executed by cropping all four corners and cropping the center of an image.
These crops and their horizontal mirrors are processed through a network to produce 10 predicted class probability distributions.
We average these distributions to compute the ﬁnal prediction.
Of course, a prediction informed by 10-crops rather than a single central crop is more accurate.
Ideally, this revised prediction should be more robust too.
However, the gains in mCE do not outpace the gains in accuracy on a
ResNet-50.
In all, 10-crop classiﬁcation is a computationally expensive option which contributes to classiﬁcation accuracy but not noticeably to robustness.
Smaller Models.
All else equal, “simpler” models often generalize better, and “simplicity” frequently translates to model size.
Accordingly, smaller models may be more robust.
We test this hypothesis with CondenseNets (Huang et al., 2017a).
A CondenseNet attains its small size via sparse convolutions and pruned ﬁlter weights.
An off-the-shelf CondenseNet ( C = G = 4 ) obtains a 26.3% error rate and a 80.8% mCE.
On the whole, this CondenseNet is slightly less robust than larger models of similar accuracy.
Even more pruning and sparsiﬁcation yields a CondenseNet ( C = G = 8 ) with both deteriorated performance (28.9% error rate) and robustness (84.6% mCE).
Here again robustness is worse than larger model robustness.
Though models fashioned for mobile devices are smaller and in some sense simpler, this does not improve robustness.
G A S EPARATE T YPE OF R OBUSTNESS
Another goal for machine learning is to learn the fundamental structure of categories.
Broad categories, such as “bird,” have many subtypes, such as “cardinal” or “bluejay.”
Humans can observe previously unseen bird species yet still know that they are birds.
A test of learned fundamental structure beyond superﬁcial features is subtype robustness .
In subtype robustness we test generalization to unseen subtypes which share share essential characteristics of a broader type.
We repurpose the
ImageNet-22K dataset for a closer investigation into subtype robustness.
[Figure]
ImageNet-22K.
A natural image dataset with a hierarchical taxonomy and numerous types and subtypes is ImageNet-22K, an ImageNet1K superset.
In this subtype robustness experiment, we manually select 25 broad types from
ImageNet-22K, listed in the next paragraph.
Each broad type has many subtypes.
We call a subtype “seen” if and only if it is in ImageNet1K and a subtype of one of the 25 broad types.
The subtype is “unseen” if and only if it is a subtype of the 25 broad types and is from ImageNet22K but not ImageNet-1K.
In this experiment, the correct classiﬁcation decision for an image of a subtype is the broad type label.
We take pretrained ImageNet-1K classiﬁers which have not trained on unseen subtypes.
Next we ﬁne-tune the last layer of these pre-trained ImageNet-1K classiﬁers on seen subtypes so that they predict one of 25 broad types.
Then, we test the accuracy on images of seen subtypes and on images of unseen subtypes.
Accuracy on unseen subtypes is our measure of subtype robustness.
Seen and unseen accuracies are shown in Figure 9, while the ImageNet-1K classiﬁcation accuracy before ﬁne-tuning is on the horizontal axis.
Despite only having 25 classes and having trained on millions of images, these classiﬁers demonstrate a subtype robustness performance gap that should be far less pronounced.
We also observe that the architectures proposed so far hardly deviate from the trendline.
The 25 broad types which we selected from ImageNet are as follows.
Amphibian (n01627424),
Appliance (n02729837), Aquatic Mammal (n02062017), Bird (n01503061), Bear (n02131653),
[Table]