Fast smooth rank function approximation based on matrix tri-factorization

Wang, Hengyou; Cen, Yigang; Zhao, Ruizhen; Voronin, Viacheslav; Wang, Yanhong

doi:10.1016/j.neucom.2016.11.068

Cited by 7 publications

(6 citation statements)

References 26 publications

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“…In this paper, we propose to formulate the problem of removing adversarial noise from attacked images while preserving important semantic structure information for successful recognition as a low-rank matrix completion problem [12,13]. Specifically, let X = [x ij ] m×n ∈ R m×n be the original image of size m × n. M = [m ij ] m×n is the random binary mask.…”

Section: Rank Function Minimizationmentioning

confidence: 99%

Structure-Oriented Progressive Low-Rank Image Restoration for Defending Adversarial Attacks

Zhao

Wang

Sun

et al. 2021

2021 IEEE International Conference on Multimedia and Expo (ICME)

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Deep neural networks recognize objects by analyzing local image details and summarizing their information along the inference layers to derive the final decision. Because of this, they are prone to adversarial attacks. On the other hand, human eyes recognize objects based on their global structures and semantic cues, instead of local image textures. In this work, we propose to develop a structure-oriented progressive low-rank image completion method to remove unneeded texture details from the input images and shift the bias of deep neural networks towards global object structures and semantic cues. We formulate the problem into a low-rank matrix completion problem with progressively smoothed rank functions to avoid local minimums. Our experimental results demonstrate the proposed method is able to successfully remove the insignificant local image details while preserving important global object structures.

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Section: Rank Function Minimizationmentioning

confidence: 99%

Structure-Oriented Progressive Low-Rank Image Restoration for Defending Adversarial Attacks

Zhao

Wang

Sun

et al. 2021

2021 IEEE International Conference on Multimedia and Expo (ICME)

Self Cite

View full text Add to dashboard Cite

Deep neural networks recognize objects by analyzing local image details and summarizing their information along the inference layers to derive the final decision. Because of this, they are prone to adversarial attacks. On the other hand, human eyes recognize objects based on their global structures and semantic cues, instead of local image textures. In this work, we propose to develop a structure-oriented progressive low-rank image completion method to remove unneeded texture details from the input images and shift the bias of deep neural networks towards global object structures and semantic cues. We formulate the problem into a low-rank matrix completion problem with progressively smoothed rank functions to avoid local minimums. Our experimental results demonstrate the proposed method is able to successfully remove the insignificant local image details while preserving important global object structures.

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“…Semantic structures of objects and images are inherently low rank [27]. Recently, methods for low-rank matrix approximation have been developed to characterize the lowrank structures in images [7,12,28,33,29]. In this paper, we propose to formulate the problem of removing adversarial noise from attacked images while preserving important semantic structure information for successful recognition as a low-rank matrix completion problem.…”

Section: Low-rank Image Completion Based On Nuclear Norm Minimizationmentioning

confidence: 99%

Structure-Preserving Progressive Low-rank Image Completion for Defending Adversarial Attacks

Zhao¹,

Wang²,

Sun³

et al. 2021

Preprint

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Deep neural networks recognize objects by analyzing local image details and summarizing their information along the inference layers to derive the final decision. Because of this, they are prone to adversarial attacks. Small sophisticated noise in the input images can accumulate along the network inference path and produce wrong decisions at the network output. On the other hand, human eyes recognize objects based on their global structure and semantic cues, instead of local image textures. Because of this, human eyes can still clearly recognize objects from images which have been heavily damaged by adversarial attacks. This leads to a very interesting approach for defending deep neural networks against adversarial attacks. In this work, we propose to develop a structure-preserving progressive low-rank image completion (SPLIC) method to remove unneeded texture details from the input images and shift the bias of deep neural networks towards global object structures and semantic cues. We formulate the problem into a low-rank matrix completion problem with progressively smoothed rank functions to avoid local minimums during the optimization process. Our experimental results demonstrate that the proposed method is able to successfully remove the insignificant local image details while preserving important global object structures. On black-box, gray-box, and whitebox attacks, our method outperforms existing defense methods (by up to 12.6%) and significantly improves the adversarial robustness of the network.

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“…In this regard, a logical solution for search work on constructing and processing images of a helical surface would be to study the accuracy at a given step discreteness when moving the camera. Another approach is to determine the nodal points that limit the search area with local discontinuities in the image brightness values that arise at the boundaries of objects [70][71][72][73][74][75].…”

Section: Introductionmentioning

confidence: 99%

New indicators and standards for measuring of the end mill's helical groove by image processing

Pivkin,

Ershov,

Grechishnikov

et al. 2023

Optical Metrology and Inspection for Industrial Applications X

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The studies will be carried out using optical metrology methods on a Walter Helicheck inspection machine in reflected light and a number of images were stored to form a statistical sample. Established new indicators and criteria for grinding efficiency based on image processing of the helical groove of the end mill. As a result, recommendations for the selection of optical control techniques were made for the first time at the intermediate stage of technological preparation for production, in real time, and after processing. In this work, for the first time, we prove the possibility of determining the camera displacement pith distance during continuous scanning of the profile of a helical surface in a radial section, the measurement accuracy and recreating a three-dimensional model of the object. As a result of the work of the new algorithm using the Haar-wavelet with new indicators, it was established that the actual one is located inside the focal zone, which proves the possibility of applied application of the method of monitoring the shape of helical flute of end mills using computer vision. The measurement accuracy of the helical flute increased from 4 to 12% along its profile.

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Fast smooth rank function approximation based on matrix tri-factorization

Cited by 7 publications

References 26 publications

Structure-Oriented Progressive Low-Rank Image Restoration for Defending Adversarial Attacks

Structure-Oriented Progressive Low-Rank Image Restoration for Defending Adversarial Attacks

Structure-Preserving Progressive Low-rank Image Completion for Defending Adversarial Attacks

New indicators and standards for measuring of the end mill's helical groove by image processing

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