Paper Citation Record · LEDGER

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks

As of 17 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2411.15246.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.15246 v2

Coverage vector

measured 40 of 40 reference resolution

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measured 40 of 40 standing notices

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

40 of 40 outbound references displayed

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External citation measurements

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Outbound references

Observation bf130cee-919a-44e5-a251-d77de5f231d7 · outbound

This paper cites Synthesizing robust adversarial examples.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Synthesizing robust adversarial examples

Reference 1

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Observation 70c8d10f-a7ef-4dbc-b6ad-21f97038009e · outbound

This paper cites Impact of low-bitwidth quantization on the ad- versarial robustness for embedded neural networks.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Impact of low-bitwidth quantization on the ad- versarial robustness for embedded neural networks

Reference 2

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Observation 430b6510-8af5-42ba-b97d-e0b2c1c1d601 · outbound

This paper cites Adversarial patch.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Adversarial patch

Reference 3

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This paper cites Towards evaluating the robustness of neural networks.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Towards evaluating the robustness of neural networks

Reference 4

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Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Unresolved cited work

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Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Unresolved cited work

Reference 6

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Observation 2cba24d7-a2bf-4abe-8367-8e319fde861c · outbound

This paper cites Double-win quant: Aggressively winning ro- bustness of quantized deep neural networks via random pre- cision training and inference.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Double-win quant: Aggressively winning ro- bustness of quantized deep neural networks via random pre- cision training and inference

Reference 7

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Observation 261e00a4-ce71-4302-adb3-82fd8614171e · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Explaining and Harnessing Adversarial Examples

Reference 8

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Observation d61b094f-6562-4809-a5f5-82e06b3c71b1 · outbound

This paper cites Physical adversarial attacks for camera-based smart systems: Current trends, categorization, applications, research challenges, and future outlook.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Physical adversarial attacks for camera-based smart systems: Current trends, categorization, applications, research challenges, and future outlook

Reference 9

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Observation fee22c45-1e94-48df-9445-e955c2be000f · outbound

This paper cites Dap: A dynamic adversarial patch for evading person detectors.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Dap: A dynamic adversarial patch for evading person detectors

Reference 10

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Observation 851eee87-d2ec-481b-9e7a-c131c2bb9ec5 · outbound

This paper cites Deep residual learning for image recognition.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Deep residual learning for image recognition

Reference 11

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Observation bd588f22-0dff-40c3-8854-99b9677813bc · outbound

This paper cites Optimizing convolutional neural networks for iot devices: performance and energy efficiency of quantization tech- niques.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Optimizing convolutional neural networks for iot devices: performance and energy efficiency of quantization tech- niques

Reference 12

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Observation 7e821afc-c0d5-49fd-8e71-5027ee5f804d · outbound

This paper cites Binarized neural networks.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Binarized neural networks

Reference 13

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This paper cites Accurate post training quantization with small calibration sets.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Accurate post training quantization with small calibration sets

Reference 14

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This paper cites Quantization and training of neural networks for efficient integer-arithmetic-only inference.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Quantization and training of neural networks for efficient integer-arithmetic-only inference

Reference 15

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Observation a1013072-6a34-4cf4-937e-1f9956afac4a · outbound

This paper cites Fedqnn: A computation–communication-efficient federated learn- ing framework for iot with low-bitwidth neural network quantization.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Fedqnn: A computation–communication-efficient federated learn- ing framework for iot with low-bitwidth neural network quantization

Reference 16

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Observation c50637f6-0ff3-4b36-9be2-e1c0f0df851c · outbound

This paper cites Lavan: Localized and visible adversarial noise.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Lavan: Localized and visible adversarial noise

Reference 17

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Observation 903cb7de-5e3f-4a10-aaf3-5e5ad77954da · outbound

This paper cites A survey on approx- imate edge ai for energy efficient autonomous driving ser- vices.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks A survey on approx- imate edge ai for energy efficient autonomous driving ser- vices

Reference 18

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Observation 9e31088e-dd7a-4982-8f6c-7ada6d99005e · outbound

This paper cites Learning multiple layers of features from tiny images.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Learning multiple layers of features from tiny images

Reference 19

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This paper cites Imagenet classification with deep convolutional neural net- works.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Imagenet classification with deep convolutional neural net- works

Reference 20

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Observation 5baabc74-ca7b-482c-b620-c49326dfaf13 · outbound

This paper cites Investigating the Impact of Quantization on Adversarial Robustness.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Investigating the Impact of Quantization on Adversarial Robustness

Reference 21

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Observation 7f671bdb-2ea0-471b-9a97-7e82c7c05bd4 · outbound

This paper cites Generative Dynamic Patch Attack.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Generative Dynamic Patch Attack

Reference 22

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This paper cites Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks

Reference 23

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This paper cites BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction

Reference 24

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This paper cites Flexi-compression: a flexible model compression method for autonomous driving.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Flexi-compression: a flexible model compression method for autonomous driving

Reference 25

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This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Swin transformer: Hierarchical vision transformer using shifted windows

Reference 26

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This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 27

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This paper cites Up or down? adap- tive rounding for post-training quantization.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Up or down? adap- tive rounding for post-training quantization

Reference 28

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This paper cites A White Paper on Neural Network Quantization.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks A White Paper on Neural Network Quantization

Reference 29

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Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Xnor-net: Imagenet classification using bi- nary convolutional neural networks

Reference 30

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This paper cites EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness against Adversarial Attacks.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness against Adversarial Attacks

Reference 31

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This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 32

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Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Rethinking the inception archi- tecture for computer vision

Reference 33

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This paper cites Neural network quan- tization in federated learning at the edge.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Neural network quan- tization in federated learning at the edge

Reference 34

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This paper cites QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization

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This paper cites Quantization aware attack: Enhancing transferable ad- versarial attacks by model quantization.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Quantization aware attack: Enhancing transferable ad- versarial attacks by model quantization

Reference 36

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This paper cites Medq: Lossless ultra-low-bit neural network quantization for medical image segmentation.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Medq: Lossless ultra-low-bit neural network quantization for medical image segmentation

Reference 37

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This paper cites DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients

Reference 38

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This paper cites The main idea is to create deformable and dynamic patches that can adapt their form to exploit the vulnerabilities of neural networks more effectively.

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks The main idea is to create deformable and dynamic patches that can adapt their form to exploit the vulnerabilities of neural networks more effectively

Reference 40

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Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Unresolved cited work

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Pith citing papers

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