Paper Citation Record · LEDGER

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

As of 15 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

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:59:42.655993Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

40 of 40 outbound references displayed

verified exact2
verified fuzzy21
unresolved17
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:59:43.120234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:59:43.109011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:59:43.097365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b1417aae-2b52-4167-a318-33f660ef713b · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:59:42.497137Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4950bb9-67b4-4ad0-843b-f19882df86b3 · outbound

This paper cites an unresolved cited work.

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

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-12T14:59:43.078489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e90df55a-5602-4951-a034-85ccb248bab3 · outbound

This paper cites an unresolved cited work.

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

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-12T14:59:43.067117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:59:43.056497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:59:42.513357Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:59:43.045587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:59:43.035254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:59:42.523976Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:59:43.016376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:59:43.004360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b8463195-102a-4adf-a424-b57ca10372eb · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:59:42.992391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 66c89436-b48f-4591-a715-1bb41b7ea2f0 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:59:42.980782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:59:42.968997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:59:42.957118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:59:42.945525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:59:42.552287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50d0838b-8197-47f3-957c-6f289c1708e6 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:59:42.927208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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

Resolution: verified exact; local_arxiv, observed 2026-08-12T14:59:42.788713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:59:42.564488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3aea6948-aefd-47e6-a064-c93dfcdc2f3f · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:59:42.568399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91a55bee-e0b4-4d58-b182-4c0053c8bcab · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:59:42.590186Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 286455ed-3c6e-4422-b20f-bed41737e1a4 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:59:42.915491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d205964c-9b04-4f78-9935-e09ea25ef2e6 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:59:42.596544Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e4d9c25-1951-42a1-b8eb-2f7ceb936006 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:59:42.599572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6ead185-7f79-4442-a988-dcfde5519ef3 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:59:42.605212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1341ec07-475f-4cca-9bb5-5bbd63041fb7 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:59:42.609388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9e18ba0-f45c-443d-95e3-eb4304b7aec7 · outbound

This paper cites Xnor-net: Imagenet classification using bi- nary convolutional neural networks.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:59:42.885583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8ed226f1-7204-4fc7-8d35-af6eff38363e · outbound

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

Resolution: verified exact; local_arxiv, observed 2026-08-12T14:59:42.723492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1d60f114-ff3a-4273-862b-208c52392665 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:59:42.623569Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b2de6df-6e17-4229-983f-a15d4f33da73 · outbound

This paper cites Rethinking the inception archi- tecture for computer vision.

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

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:59:42.873852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 734691c2-34bd-4238-8f58-dede5470a610 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:59:42.861578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8928d2df-5881-4661-b8ad-14eb4990bf31 · outbound

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

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-12T14:59:42.635336Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c5bc2b2-0c1f-4ef8-9656-4f025a2fc11a · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:59:42.847722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 279a700c-0cd1-4414-afc8-b6edde245ae3 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:59:42.835028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ceaf79f3-8073-48b2-830d-a6ed4dbe76b9 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-12T14:59:42.647357Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 807b6ebb-25bd-477f-aba1-593377b44a67 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:59:42.810688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 10d0b572-3cc1-4086-9ffc-543de2cde23f · outbound

This paper cites an unresolved cited work.

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

Reference 255

Resolution: unresolved; raw_fallback, observed 2026-08-12T14:59:42.822868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

No inbound Pith citation observations are available.