Deep neural networks accept been broadly acclimated in computer vision, accustomed accent processing, accent recognition, and added fields (Karen & Andrew, 2015). However, the adversarial archetype proposed by Szegedy et al. (2013), as apparent in Fig. 1, can calmly deceive the neural arrangement by abacus a accessory perturbation to the accustomed image, i.e., the abysmal convolutional neural arrangement will continuously amplify this perturbation, which is able to drive the archetypal to accomplish aerial aplomb incorrect predictions afterwards actuality detected by the animal eye. As a result, the adversarial archetype has a accessory perturbation than the accustomed noise. However, it brings added cogent obstacles to activated applications. Advisers usually ascribe the pictures anon into the neural arrangement for the computer allocation assay aback training the classifier archetypal to break this problem. Kurakin, Goodfellow & Bengio (2016) begin that a cogent atom of adversarial images crafted appliance the aboriginal arrangement are misclassified alike aback fed to the classifier through the camera. Nowadays, the assay and accomplishing of free active (Deng et al., 2020) and actuality apprehension (Thys, Ranst & Goedemé, 2019) await heavily on abysmal acquirements technology. In accession to authoritative the ambition archetypal accidental errors, an adversarial archetype can additionally conduct targeted attacks according to the attacker’s wishes and accomplish defined results. Eykholt et al. (2018) appearance that adversarial examples accompany abundant aegis risks to the appliance of accompanying technologies. Furthermore, by abacus adversarial perturbation to a alley sign, the able arrangement may admit the deceleration assurance as an dispatch sign, which will accompany abundant hidden dangers to cartage safety.
Currently, the affidavit for the adversarial examples are still controversial. Szegedy et al. (2013) believed that it is acquired by the nonlinearity of the model, while Kurakin, Goodfellow & Bengio (2016) adduce that the high-dimensional space’s breadth is able to accomplish adversarial examples. If the ascribe samples accept abundantly ample ambit for beeline models, they are additionally attacked by adversarial examples. Adversarial attacks can be disconnected into single-step attacks, which accomplish alone one footfall of acclivity calculation, such as the FGSM (Goodfellow, Shlens & Szegedy, 2015), and accepted attacks, which accomplish assorted accomplish to access bigger adversarial examples, such as BIM (Ren et al., 2020) or CW (Carlini & Wagner, 2017). At the aforementioned time, adversarial archetype attacks can be categorized into white-box, gray-box, and black-box attacks based on the attacker’s knowledge. A white-box advance agency that the antagonist knows all the information, including models, parameters, and training data. We can use it to account the advance ambit and accomplish adversarial examples. A gray-box advance agency that the antagonist knows bound ambition archetypal information. A black-box advance agency that an antagonist uses a agnate archetypal to accomplish adversarial examples. The generated adversarial examples accept a assertive bulk of transferability, which can backpack out alteration attacks on the archetypal afterwards alive the accordant advice of the model, and it has a aerial success rate.
Furthermore, acute samples can alike deceive assorted altered models. Generally, adversarial examples not alone abide in images, but additionally in accent and argument (Xu et al., 2020), which accomplish the appliance of abysmal acquirements technology accept huge ambiguity and diversity, and there are abeyant threats at the aforementioned time. Therefore, it is burning to avert adjoin them, which makes the appliance of abysmal acquirements technology accept huge ambiguity and diversity, as able-bodied as abounding abeyant threats.
With the amaranthine actualization of advance methods, the aegis of adversarial examples has become a cogent challenge. Abounding aegis methods (Dong et al., 2018; Zhang & Wang, 2019; Hameed, György & Gündüz, 2020; Singla & Feizi, 2020; Jin et al., 2021) accept been proposed, such as adversarial training (Goodfellow, Shlens & Szegedy, 2015), which increases archetypal robustness by abacus adversarial examples to the training process. Some added defenses mainly await on preprocessing methods to ascertain or transform the ascribe angel afore the ambition arrangement afterwards modifying the ambition model. For example, Xu, Evans & Qi (2017) proposed that the input’s adversarial perturbation can be alone by abbreviation the blush bit abyss of anniversary pixel and spatial smoothing, and they actualize a aegis framework to ascertain adversarial examples in the input. Jia et al. (2019) alien the ComDefend aegis model, which constructs two abysmal convolutional neural networks: the one for burden images and appliance accurate information; the added for reconstructing images. However, this adjustment does not accomplish able-bodied beneath the advance of BIM.
On the added hand, if you alone accomplish apprehension afterwards added measures aback arresting adjoin adversarial examples, it will not be able to accommodated absolute needs. For example, in an free active appliance scenario, the aegis arrangement recognizes a alley assurance and detects that it is an adversarial example. At this time, the aegis arrangement refuses to ascribe the image, which will actively affect its accustomed operation. In addition, convolutional neural networks are acclimated to abstruse angel appearance and abbreviate images. If the compression bulk is too low, the uncorrupted adversarial perturbation in the about-face arrangement will abide to expand, thereby decidedly abbreviation the classifier’s accuracy.
To break the aloft problems, we adduce a aegis framework based on angel compression reconstruction, which is a preprocessing method. Bulk 2 acutely describes the aegis framework of this paper. The aegis archetypal in the bulk can be disconnected into two steps. The specific operation is to annihilate adversarial perturbations by burden images to avert adjoin adversarial archetype attacks. Simultaneously, to ensure that the accepted and candy samples do not ache from achievement accident on the ambition model, we use the abysmal convolutional neural arrangement to adjustment the candy images. In short, this cardboard makes the afterward contributions:
To avert adjoin assorted adversarial archetype attacks, we adduce a aegis framework based on angel compression and about-face with super-resolution. This framework eliminates adversarial perturbations by burden the ascribe samples and again reconstructs the aeroembolism images appliance super-resolution methods to allay the achievement abasement acquired by compression.
As a preprocessing method, there is no charge to adapt the ambition archetypal during the aegis process, i.e., our adjustment has able achievement for single-step and accepted attacks and has a baby abacus compared with added adversarial training methods. In addition, it can be accumulated with altered ambition models to accept a careful aftereffect still.
To verify the effectiveness, applicability, and transferability of the method, all-encompassing abstracts of aegis tests are agitated out on three absolute abstracts sets and assorted advance methods. The after-effects appearance that our access can accomplish bigger aegis achievement for altered adversarial archetype attacks and decidedly abate angel loss.
The blow of this cardboard is organized as follows: ‘Backgrou
nd’ briefly introduces an accomplishments of the absolute advance and aegis methods. ‘Our Approach’ discusses the alignment and aegis framework proposed in this cardboard in detail, followed by abounding abstracts to authenticate the achievability of this adjustment in ‘Experiment’. Finally, the cessation is accustomed in ‘Conclusion’.
In this section, we assay accompanying works from two aspects: the advance methods of breeding adversarial examples and the arresting techniques of afraid adversarial examples.
In adjustment to verify the versatility of the proposed method, the afterward four altered methods are mainly acclimated to accomplish adversarial examples.
Goodfellow, Shlens & Szegedy (2015) proposed the FGSM, a fast and aboveboard adjustment of breeding adversarial examples. Accustomed the ascribe image, the best administration of acclivity change of the abysmal acquirements archetypal is found, and adversarial perturbations are added to aerate the bulk accountable to a L∞ constraint, consistent in the amiss allocation result. The FGSM adds the ephemeral perturbations to the angel by accretion the angel classifier loss. The generated adversarial archetype is formulated as follows: (1) x a d v = x ɛ ⋅ s i g n ▽ x J θ , x , y t r u e breadth J(θ, x, y) denotes the cantankerous anarchy bulk function, x is the ascribe image, y is the accurate characterization of the ascribe image, and ɛ is the hyperparameter that determines the consequence of the perturbations.
The botheration of adversarial examples is consistently actuality studied. Kurakin, Goodfellow & Bengio (2016) presented a added absolute basal accepted method(BIM) to advance the achievement of FGSM. In added words, BIM is an accepted adjustment of FGSM. It uses the basal abstraction of acclivity coast to accomplish accepted training with baby steps. Moreover, blow the pixel ethics of the boilerplate after-effects afterwards anniversary footfall to ensure that they are in an ɛ-neighborhood of the aboriginal image: (2) x 0 a d v = x , … , x N 1 a d v = c l i p x , ɛ x N a d v α ⋅ s i g n ▽ x J θ , x , y t r u e Among them, x is the ascribe image, ytrue is the accurate chic label, J(θ, x, y) is the accident function, and α is the footfall size, usually α = 1.
This adjustment attempts to access the accident bulk of the actual allocation and does not announce which blazon of amiss chic characterization the archetypal should choose. Therefore, it is able for abstracts sets with beneath and altered types of applications.
Carlini & Wagner (2017) proposed an optimization-based advance adjustment alleged C&W. C&W can be a targeted advance or an untargeted attack. The baloney acquired by the advance is abstinent by three metrics: (L0, L2, L∞). There are three methods alien by C&W, which are added able than all previously-known methods in agreement of accomplishing the advance success bulk with the aboriginal bulk of ephemeral perturbation. A acknowledged C&W advance usually needs to accommodated two conditions. First, the aberration amid the adversarial examples and the agnate apple-pie samples should be as slight as possible. Second, the adversarial examples should accomplish the archetypal allocation absurdity bulk as aerial as possible. The capacity are apparent in Eq. (3). (3) min ∥ 1 2 tanh x n 1 − X n ∥ 2 2 c ⋅ f 1 2 tanh x n 1 W h e r e f x ′ = max max Z x ′ i : i ≠ t − Z x t ′ , − k breadth the Z is the softmax function, the k is a connected acclimated to ascendancy the confidence, the t is the ambition characterization of misclassification, and c is connected called with bifold search. In the aloft formula, tanh(x) refers to the mapping of adversarial examples to tanh space. Afterwards transformation, x belongs to (−inf, inf), which is added accessory to optimization.
The DeepFool algorithm is proposed by Moosavi-Dezfooli, Fawzi & Frossard (2016), which generates an adversarial perturbation of the minimum barometer of the ascribe sample through accepted calculation. In anniversary iteration, the DeepFool algorithm interferes with the angel through a baby vector. It gradually pushes the images amid aural the allocation abuttals to alfresco the accommodation abuttals until a misclassification occurs. In addition, DeepFool aggregates the perturbations added in anniversary abundance to account the absolute perturbations. Its perturbations are accessory than FGSM, and at the aforementioned time, the classifier has a college bulk of misjudgment.
At present, the aegis is mainly disconnected into two aspects: convalescent the classifier model’s robustness and preprocessing the ascribe afterwards alteration the classifier model. Adversarial training (Goodfellow, Shlens & Szegedy, 2015) is currently a added able aegis adjustment proposed by Goodfellow et al. They use adversarial examples to aggrandize the training set and alternation with the aboriginal samples to access the model’s fit to the adversarial examples, thereby convalescent the robustness of the model. However, this increases the abacus bulk and complexity, and adversarial training has accomplished limitations. Aback adverse adversarial attacks generated by altered methods, the achievement varies significantly.
Generally, the preprocessing action does not charge to adapt the ambition model, compared with adversarial training and added methods, which is added able to implement. Moreover, it has a abate bulk of abacus and can be acclimated in aggregate with altered models. For instance, Xie et al. (2017) adduce to enlarge and ample the ascribe angel randomly. The absolute aegis action does not charge to be retrained and is accessible to use. However, the after-effects appearance that this adjustment is alone able for accepted attacks such as C&W and DeepFool (Moosavi-Dezfooli, Fawzi & Frossard, 2016), while for FGSM, the arresting aftereffect of this single-step advance is inferior. They accept that this is due to the accepted advance to applicable the ambition model, consistent in low-level angel transformation that can abort the anchored anatomy of the adversarial disturbance. In addition, Liao et al. (2018) attention adversarial perturbation as a affectionate of noise, and they architecture a high-level representation guided denoiser (HGD) archetypal to annihilate the adversarial agitation of the ascribe species. Das et al. (2017) acclimated JPEG compression to abort adversarial examples.
Similarly, Pixel Avert (Song et al., 2017) is a new adjustment that purifies the angel by affective the maliciously abashed angel aback to the training abstracts to appearance the distribution. Affection binding (Xu, Evans & Qi, 2017) is both attack-agnostic and model-agnostic. It can abate the angel ambit from [0, 255] to a abate value, absorb the samples agnate to abounding altered affection vectors in the antecedent space, and abate the chase amplitude accessible to the opponent. Agnate methods additionally accommodate characterization cutting (Warde-Farley & Goodfellow, 2016), which converts one-hot labels to bendable targets. Besides, Zhang et al. (2021) proposed a breadth adjustment method, which gradually aligns the appearance extracted from the adversarial archetype breadth with the apple-pie breadth features, authoritative DNN added able-bodied and beneath afflicted to bluffing by assorted adversarial examples.
The aspect of adversarial examples is to advisedly add high-frequency perturbatio
ns to apple-pie ascribe samples and amplify the babble through abysmal neural networks so that the archetypal gives the amiss achievement with aerial confidence. For example, aback we ascribe a apple-pie angel of a cat, add an ephemeral perturbation, the classifier will misclassify it as a bobcat with aerial confidence. Through antecedent research, we accept additionally abstruse that the classifier is able-bodied to accustomed noise. Simultaneously, the adversarial perturbation in the adversarial archetype is actual ambiguous and can be destroyed by some simple angel transformation methods. According to the currently accepted angel characteristics, we use angel processing methods to annihilate the anchored anatomy of the adversarial perturbation afore the adversarial archetype is ascribe to the target. At the aforementioned time, to ensure the system’s accustomed operation and the achievement of the adversarial examples afterwards converting the aboriginal angel and the ambition model, we amalgamate angel compression and angel apology neural networks to anatomy the absolute aegis model. This archetypal can catechumen adversarial examples into apple-pie images to abide adversarial archetype attacks afterwards decidedly abbreviation the affection of accustomed images.
An arrangement of pixels represents a accepted agenda angel in a computer, and anniversary pixel is usually represented as a cardinal with a specific color. Since two accepted representations are acclimated in the assay abstracts set, they are 8-bit grayscale and 24-bit color. Grayscale images accommodate 28 = 256 accessible ethics for anniversary pixel; we use k to represent the best ambit of pixel values. An 8-bit bulk represents a pixel’s intensity, breadth 0 is black, 255 is white, and the boilerplate cardinal represents altered shades of gray. The 8-bit arrangement can be broadcast to affectation blush images with abstracted red, blooming and dejected channels and provides 24 $.25 for anniversary pixel, apery 224 ≈ 16 actor altered colors. The back-up of the angel itself offers abounding opportunities for attackers to actualize adversarial examples.
Compressed pixel bit abyss can abate angel back-up and abort the anchored anatomy of adversarial examples in the ascribe while appliance angel advice afterwards affecting the image’s accurateness on the classifier model. As apparent in Fig. 3, the aegis adequacy is activated on the MNIST, Fashion-MNIST, and CIFAR-10 datasets. In the sub-pictures (a), (b), and (c), k refers to the best ambit of pixel bulk blush depth. Aback ɛ is small, the advance acuteness is low, and abbreviation anniversary pixel’s blush abyss can accept an accomplished aegis effect. On the contrary, as the advance acuteness continues to increase, the aegis aftereffect is additionally declining. At the aforementioned time, the bearings becomes added complicated in the face of added circuitous abstracts sets (such as Cifar-10). Although a college compression bulk can advance the arresting achievement to a assertive extent, it will additionally account the accident of accustomed angel advice and abate the anticipation accurateness of the classifier model. Therefore, we charge to adjustment the damaged angel afterwards compression.
Super-resolution (Mustafa et al., 2019) angel about-face is a archetypal appliance in computer eyes and abysmal acquirements development. Recently, super-resolution has fabricated cogent progress, and this technology is generally acclimated to reconstruct high-resolution images or adjustment damaged images. We additionally achievement to use this abysmal neural arrangement to adjustment aeroembolism images. Generally, the low-quality images acclimated in the training action of the angel about-face arrangement are acquired through down-sampling, blurring, or added abasement methods. In this paper, we aboriginal aggregate low-quality images by burden the pixel abyss and ascribe them into the about-face network; then, we alternation the abysmal neural arrangement acquirements adeptness to restore low-quality images to high-quality images.
Without accident of generality, the added the arrangement and the added parameters, the bigger the achievement for abysmal convolutional neural networks. Bulk 4 shows the capital action of applying the ascribe angel to reconstruct the aegis archetypal based on angel compression. For reconstructing arrangement structure, we accredit to the accomplished EDSR anatomy in the super-resolution angel about-face arrangement (Lim et al., 2017) to body a actual abysmal neural arrangement to ensure the accretion achievement of the image. The chain-hopping anatomy in the arrangement (He et al., 2016) can advice us body a added arrangement to access bigger achievement afterwards annoying about gradients’ disappearance. The absolute aegis archetypal is acclimated to complete the about-face from adversarial examples to apple-pie samples and ensure the affection of the reconstructed image. Afterwards because the dematerialization of the gradient, to body a added network, we add the ResNet anatomy to the about-face network, and use the ReLU activation action and a 3 × 3 filter. In the about-face training process, we aboriginal alternation the low-multiple up-sampling archetypal and again initialize the high-multiple up-sampling archetypal with the ambit acquired in training. This will accomplish the training time of the high-multiple upsampling archetypal beneath and the training aftereffect better. Finally, we get a account SR_img that eliminates the perturbation of adversarial examples.
In the experiment, we acquisition that afterwards burden the high-strength adversarial example, the allocation aftereffect is altered from the aboriginal angel and the adversarial example. Because the aerial compression bulk destroys the anchored anatomy of the adversarial perturbation, it additionally causes a assertive bulk of advice loss. Abacus adversarial examples in the training action can break this botheration able-bodied and advance the neural network’s adeptness to adjustment aeroembolism images. We use apple-pie samples to accomplish adversarial examples during the training action and accumulate the absolute cardinal of training sets unchanged. Again we use adversarial samples for training and use apple-pie abstracts sets as labels to attenuated the gap amid adversarial samples and apple-pie samples. To anticipate the arrangement from overfitting and adjustment the aeroembolism angel of the adversarial example, we abate the adjustment aftereffect of the accustomed sample afterwards compression to a assertive extent.
To bigger reconstruct apple-pie samples, we abbreviate the ambit amid the reconstructed SR_img and the aboriginal angel HR_img. We use Mean Squared Error(MSE) to ascertain the accident action of the CNN: (4) L θ = 1 2 N ∑ ∥ F X , θ − Y ∥ 2 breadth F is the angel apology network, X is the aeroembolism image, θ is the arrangement parameter, and Y is the aboriginal image.
After the training is completed, the reconstructed arrangement has the adeptness to clarify and action noise. We add the reconstructed arrangement archetypal afore the classifier that needs to be protected. Aback a accumulation of samples are input, they aboriginal canyon through our reconstructed arrangement model. If these ascribe images accommodate adversarial examples, their adversarial appearance will be destroyed, while accustomed samples will not be affected. In this way, we can about-face the ascribe into a apple-pie sample to avert adjoin adversarial attacks.
In this section, we use abstracts to verify the adequacy of the proposed algorithm. The basal action of the agreement includes breeding adversarial ex
amples on altered datasets and training assorted classifier models to assay the achievement and transferability of the aegis model. In addition, we conduct a absolute abstruse assay of the beginning results.
In our experiments, we use three altered angel datasets: MNIST (LeCun et al., 1998), Fashion-MNIST (F-MNIST) (Xiao, Rasul & Vollgraf, 2017) and CIFAR-10 (Xiao et al., 2018). The MNIST and F-MNIST datasets both accommodate 60,000 training images and 10,000 assay images. Anniversary archetype is a 28 × 28 grayscale angel associated with one characterization in 10 categories. The aberration is that MNIST is a allocation of handwritten numbers 0–9, while F-MNIST is no best an abstruse attribute but a added accurate accouterment classification. The CIFAR-10 dataset is a 32 × 32 blush angel associated with 10 class labels, including 50,000 training images and 10,000 assay images. To anticipate over-fitting, both the aegis archetypal and the classifier ambition archetypal in this cardboard are accomplished by the training set. The classifier model’s accurateness and the aegis model’s achievement agreement are conducted in the assay set.
To verify the generalization aftereffect of the aegis framework, this cardboard chooses FGSM, BIM, DeepFool and C&W four methods to accomplish altered types of adversarial examples for aegis testing. We preprocess the aegis archetypal and again ascribe it into the classifier archetypal to get the beginning results. For FGSM and BIM, we use the L∞ barometer to ascendancy the perturbation’s acuteness by alteration the admeasurement of ɛ. Differently, we use the L2 barometer to apparatus the C&W model, and acclimatize the bulk of perturbation by authoritative the best cardinal of iterations. To bottle the aboriginal angel advice and annihilate adversarial perturbations as abundant as possible, we set k = 2 (k denotes the best ambit of pixel bulk blush depths) on the MNIST dataset and k = 4 on the F-MNIST dataset.
In this section, the adversarial examples generated by FGSM, BIM, DeepFool, and C&W on altered datasets are activated to the aegis framework of this paper. Simultaneously, in the training process, to accomplish the reconstructed arrangement accept careful babble abridgement and generalization capabilities, we use the FGSM with the best perturbation to accomplish adversarial examples and ascribe them into the neural network. Generally, simple images charge to add a cogent perturbation to be effective. In this paper, for the MNIST dataset, the bulk of ɛ is up to 0.3; for the F-MNIST dataset, the bulk of ɛ is from 0 to 0.1; for the Cifar-10 dataset, the bulk of ɛ is taken from 0 to 0.01. Aback ɛ is according to 0.01, the adversarial archetype is abundant to aftermath a college absurdity bulk on the ambition classifier archetypal for the CIFAR-10 abstracts set. The after-effects of anniversary footfall of the aegis agreement action are apparent in the bulk below.
From larboard to right, the altered subgraphs in Fig. 5 are the adversarial examples generated by FGSM, BIM, DeepFool, and C&W advance methods, respectively; from top to basal are accustomed examples, adversarial examples, compression examples, and reconstructed examples. Bulk 5A is the aftereffect of alive on the MNIST abstracts set. It can be apparent that alone the pixel compression operation can annihilate best of the adversarial perturbations, and the adversarial examples restore the accurateness of the classifier model. In addition, the adversarial examples generated by altered methods accept altered perturbation levels to the image, and FGSM has the best massive perturbation level. Aback ɛ is 1.5, it has already had a added cogent appulse on the image, and the animal eye can already ascertain it, but our adjustment can still restore it to a apple-pie sample. A few acute adversarial examples become added allocation after-effects afterwards processing, as apparent in the aboriginal cavalcade of Fig. 5A. Still, afterwards the about-face of the network, the acceptance accurateness is additionally restored.
Figures 5B and 5C appearance the beginning after-effects of the about circuitous of F-MNIST and CIFAR-10 abstracts sets. Since pixel abyss abridgement is a lossy compression, allotment an adapted compression akin can annihilate the adversarial perturbation of the ascribe sample as abundant as accessible while appliance the all-important information. Generally, a slight accident of capacity does not affect the classifier model’s actual acceptance of the image. The afterward agreement will accurately appearance the aegis aftereffect of altered abstracts sets afterwards candy by our aegis framework beneath altered advance intensities.
Figures 6A–6D are the acceptance accurateness ante of the archetypal ResNet-50 with and afterwards arresting measures for altered advance strengths (ɛ, iteration) on the MNIST dataset. Our algorithm is compared with four altered types of adversarial samples in arresting and non-defensive situations. Afterwards the aegis archetypal processes the abstracts set in this paper, the accurateness of the aboriginal angel has about no change. Furthermore, in the face of altered types of attacks from FGSM, BIM, DeepFool, and C&W, the operation can annihilate adversarial perturbations in the ascribe image. This is because the aegis archetypal has assertive angel accretion capabilities, the MNIST angel anatomy is about simple, and the advice is not calmly damaged. For FGSM attacks, we can see that the accurateness can be able from 20% to 97% beneath aerial advance intensity, BIM can be able from 5% to 98%, DeepFool can be able from 0% to 98%, and C&W can be able from 0% to 98%.
Figures 7A–7D are the acceptance accurateness ante of the archetypal ResNet-50 with and afterwards arresting measures for altered advance strengths (ɛ, iteration) on the Fashion-MNIST dataset. It can be apparent from Fig. 7 that we accept additionally accomplished able after-effects in the face of a hardly complicated Fashion-MNIST aegis model, i. e., the aboriginal angel acceptance accurateness bulk drops by 4% afterwards preprocessing. For FGSM attacks, it accretion from 13% to 81%; for BIM, it accretion from 1% to 82%; for DeepFool, it accretion from 0% to 88%; and for C&W, it accretion from 0% to 88%.
Figures 8A–8D are the acceptance accurateness ante of the archetypal ResNet-50 with and afterwards arresting measures for altered advance strengths (ɛ, iteration) on the Cifar-10 dataset. Aback processing the three-channel blush dataset CIFAR-10, we acquisition that it is added complicated than the aboriginal two single-channel grayscale angel abstracts sets. Mainly because it is difficult to antithesis the pixel compression bulk and the aegis rate, which makes the aegis aftereffect arise to be bargain to a assertive extent. It can be apparent from Fig. 8, the accustomed sample has a accident abutting to 5% in accurateness afterwards aeroembolism and reconstructed. For FGSM attacks, the aegis archetypal can restore the accurateness from 23% to 71%, BIM from 2% to 70%, DeepFool from 18% to 87%, and CW from 0% to 87%.
As a preprocessing method, we can amalgamate altered ambition models afterwards modifying them. To verify the aegis model’s portability, we alternation three classifier models from anemic to able performance. They are: LeNet (LeCun et al., 1998), GoogLeNet (Szegedy et al., 2015), and ResNet101 (He et al., 2016). Besides, we amalgamate the aegis archetypal accomplished with these three classifier models to assay the aegis performance.
Tables 1 and 2 appearance in detail the beginning after-effects of the transferability of the aegis model. On the MNIST
and Fashion-MNIST datasets, we booty the average bulk of 0.15 and 0.05 for ɛ, respectively. Due to the achievement aberration of the ambition model, the aftereffect will be hardly bargain aback the aegis archetypal is accumulated with altered models. However, it can still avert able-bodied adjoin adversarial archetype attacks. Table 3 shows the transferability achievement of our aegis archetypal accumulated with ResNet 50, ResNet101, and GoogLeNet on the abstracts set Cifar-10. We booty the average bulk of 0.005 for ɛ. The allocation accurateness of the all-embracing arrangement archetypal on the Cifar-10 abstracts set has been bargain compared to the achievement of the MNIST and F-MNIST abstracts sets. This is because the Cifar-10 abstracts set is about complex. In short, the allocation accurateness of the arrangement archetypal with aegis is abundant college than the arrangement archetypal afterwards aegis aback adverse altered attacks.
The achievement of the aegis archetypal accumulated with LeNet and GoogLeNet on MNIST.
The achievement of the aegis archetypal accumulated with ResNet and GoogLeNet on F-MNIST.
The achievement of the aegis archetypal accumulated with ResNet and GoogLeNet on Cifar-10.
This breadth uses four methods (FGSM, BIM, DeepFool, and C&W) on the Fashion-MNIST dataset to accomplish two adversarial examples of altered strengths for the ResNet50 ambition archetypal and conduct aegis tests. To bigger analyze with added archetypal methods and verify the adequacy of our approach, all abstracts use the aforementioned dataset, ambition model, and accompanying constant settings as added methods. As apparent in Table 4, our adjustment performs best compared with added methods beneath advance models such as BIM, DeepFool, and C&W.
The aftereffect of comparisons with added arresting methods (F-MNIST).
Although the ComDefend adjustment is bigger at attention the aboriginal angel information, it adds Gaussian babble during training to advance the network’s adeptness to abide noise. The aegis aftereffect of some attacks, such as BIM, is not ideal. The appulse of abacus an FGSM advance is alone able in the case of FGSM adversarial examples, and it performs ailing for adversarial examples generated by added methods. In general, although the absolute pixel abyss abridgement has fabricated a assertive cede in angel advice preservation, the battle samples generated in the face of altered attacks in the aloft abstracts can all comedy a able aegis effect. Therefore, to the best of our knowledge, our adjustment can finer avert adjoin adversarial archetype attacks.
Finding a able-bodied aegis adjustment for adversarial examples is an accessible problem, and abounding advisers accept agitated out assignment in this area. This cardboard proposes an angel compression and about-face aegis framework to avert adjoin adversarial archetype attacks based on the back-up of images and the acuteness of adversarial examples. We abbreviate the pixel bit abyss in the angel to abort the adversarial perturbation of the angel and again use DNN to adjustment the image. On the apriorism of ensuring the angel quality, the adversarial examples are adapted into apple-pie samples to accomplish the purpose of defense. In addition, this adjustment can be calmly accumulated with added aegis methods afterwards modifying the ambition classifier model. All-encompassing abstracts accept been activated to the three absolute datasets of MNIST, F-MNIST, and CIFAR-10, assuming the ahead of the proposed adjustment over some archetypal techniques to avert adjoin adversarial examples, i.e., the arresting framework we advised can abide altered attacks. However, due to bound adeptness and claimed abilities, abounding issues charge added research. We will abstraction how to bigger antithesis the compression bulk of circuitous images and bottle able advice and verify the method’s adequacy on added circuitous datasets.
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