Paper Citation Record · LEDGER

A principled approach for generating adversarial images under non-smooth dissimilarity metrics

As of 18 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:1908.01667.

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

pith.paper-citation-record.v1
1908.01667 v2

Coverage vector

measured 38 of 38 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-14T15:11:10.285502Z

measured 38 of 38 standing notices

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Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

Reference resolution

38 of 38 outbound references displayed

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External citation measurements

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Outbound references

Observation 4b7f1e67-931d-471b-a793-78e160baa9ed · outbound

This paper cites ADef: an Iterative Algorithm to Construct Adversarial Deformations.

A principled approach for generating adversarial images under non-smooth dissimilarity metrics ADef: an Iterative Algorithm to Construct Adversarial Deformations

Reference 1

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Observation 5750e7ec-e539-40de-ba29-b3a47957fee8 · outbound

This paper cites an unresolved cited work.

A principled approach for generating adversarial images under non-smooth dissimilarity metrics Unresolved cited work

Reference 2

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Observation 1eea2bd1-726b-4087-bf16-345fdfeef0f9 · outbound

This paper cites ProxQuant: Quantized Neural Networks via Proximal Operators.

A principled approach for generating adversarial images under non-smooth dissimilarity metrics ProxQuant: Quantized Neural Networks via Proximal Operators

Reference 3

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Observation b9284f16-585c-47e7-9566-4e3cf6c559e9 · outbound

This paper cites Fast newton-type methods for total variation regularization.

A principled approach for generating adversarial images under non-smooth dissimilarity metrics Fast newton-type methods for total variation regularization

Reference 4

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Observation 68c63aa2-a919-42a1-a0c4-6942ad47f6fa · outbound

This paper cites Modular proximal optimiz ation for multidimensional total-variation regularizati on.

A principled approach for generating adversarial images under non-smooth dissimilarity metrics Modular proximal optimiz ation for multidimensional total-variation regularizati on

Reference 5

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Observation ce75ad9e-be4c-480c-9c69-0d589b661ea7 · outbound

This paper cites First-order methods in optimization.

A principled approach for generating adversarial images under non-smooth dissimilarity metrics First-order methods in optimization

Reference 6

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Observation 64d8ae57-ac4b-41c2-ac25-df84d8b1142b · outbound

This paper cites Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models.

A principled approach for generating adversarial images under non-smooth dissimilarity metrics Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models

Reference 7

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Observation 27e46986-c939-45fc-a711-5f06ba7154d1 · outbound

This paper cites Towards Evaluating the Robustness of Neural Networks.

A principled approach for generating adversarial images under non-smooth dissimilarity metrics Towards Evaluating the Robustness of Neural Networks

Reference 8

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Observation f7bcb01f-8674-4ad3-bc29-b128b165038b · outbound

This paper cites HopSkipJumpAttack: A Query-Efficient Decision-Based Attack.

A principled approach for generating adversarial images under non-smooth dissimilarity metrics HopSkipJumpAttack: A Query-Efficient Decision-Based Attack

Reference 9

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Observation 90005105-290d-417b-9e27-67abd7c231e9 · outbound

This paper cites Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark.

A principled approach for generating adversarial images under non-smooth dissimilarity metrics Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark

Reference 10

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Observation f656fe73-aa9b-4002-906c-0d22c40cae45 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

A principled approach for generating adversarial images under non-smooth dissimilarity metrics Imagenet: A large-scale hierarchical image database

Reference 11

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Observation e3b2c65e-f019-42c7-a029-7588f84a2c2e · outbound

This paper cites Efﬁcient projections onto the l1-ball for learning in high dimensions.

A principled approach for generating adversarial images under non-smooth dissimilarity metrics Efﬁcient projections onto the l1-ball for learning in high dimensions

Reference 12

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Observation a2b84e6a-5ab2-473c-83b7-900297118eaf · outbound

This paper cites Robust Physical-World Attacks on Deep Learning Models.

A principled approach for generating adversarial images under non-smooth dissimilarity metrics Robust Physical-World Attacks on Deep Learning Models

Reference 13

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This paper cites Scaleable input gradient regularization for adversarial robustness.

A principled approach for generating adversarial images under non-smooth dissimilarity metrics Scaleable input gradient regularization for adversarial robustness

Reference 14

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Observation e1781a80-1366-4478-84cf-fcdc0f7f769b · outbound

This paper cites Ob erman.

A principled approach for generating adversarial images under non-smooth dissimilarity metrics Ob erman

Reference 15

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Observation cb48b2ae-b9ae-434e-8939-55a5af0906c2 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

A principled approach for generating adversarial images under non-smooth dissimilarity metrics Explaining and Harnessing Adversarial Examples

Reference 16

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Observation d30a45ae-fee7-40f2-84c4-33da8153ae91 · outbound

This paper cites I dentity mappings in deep residual networks.

A principled approach for generating adversarial images under non-smooth dissimilarity metrics I dentity mappings in deep residual networks

Reference 17

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Observation 03a2ec74-9aae-44a8-a425-864720409c73 · outbound

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

A principled approach for generating adversarial images under non-smooth dissimilarity metrics Learning multiple layers of features from tiny images

Reference 18

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Observation febf31f9-10d6-41ce-8e98-ce3eaea53d9a · outbound

This paper cites Adversarial examples in the physical world.

A principled approach for generating adversarial images under non-smooth dissimilarity metrics Adversarial examples in the physical world

Reference 19

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This paper cites Object recognition with gradient-based learning.

A principled approach for generating adversarial images under non-smooth dissimilarity metrics Object recognition with gradient-based learning

Reference 20

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Observation 3bab4747-3b1a-40e0-9425-018f352e9f8d · outbound

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

A principled approach for generating adversarial images under non-smooth dissimilarity metrics Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 21

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Observation d541dffb-cd87-4008-af2f-40e44d65129a · outbound

This paper cites Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems.

A principled approach for generating adversarial images under non-smooth dissimilarity metrics Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems

Reference 22

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Observation 14950efc-98d9-4ef7-b42c-2a0b20a9f582 · outbound

This paper cites SparseFool: a few pixels make a big difference.

A principled approach for generating adversarial images under non-smooth dissimilarity metrics SparseFool: a few pixels make a big difference

Reference 23

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This paper cites DeepFool: a simple and accurate method to fool deep neural networks.

A principled approach for generating adversarial images under non-smooth dissimilarity metrics DeepFool: a simple and accurate method to fool deep neural networks

Reference 24

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A principled approach for generating adversarial images under non-smooth dissimilarity metrics Numerical optimization

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Observation 81ff1c1f-3dbb-440c-bef4-ccefb2a4971e · outbound

This paper cites The Limitations of Deep Learning in Adversarial Settings.

A principled approach for generating adversarial images under non-smooth dissimilarity metrics The Limitations of Deep Learning in Adversarial Settings

Reference 26

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A principled approach for generating adversarial images under non-smooth dissimilarity metrics Catalyst for gradient - based nonconvex optimization

Reference 27

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Observation 7c7e00e9-8750-497c-9c3d-eac7d11b0903 · outbound

This paper cites Foolbox: A Python toolbox to benchmark the robustness of machine learning models.

A principled approach for generating adversarial images under non-smooth dissimilarity metrics Foolbox: A Python toolbox to benchmark the robustness of machine learning models

Reference 28

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A principled approach for generating adversarial images under non-smooth dissimilarity metrics V ariational analysis, volume 317

Reference 29

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This paper cites Nonline ar total variation based noise removal algorithms.

A principled approach for generating adversarial images under non-smooth dissimilarity metrics Nonline ar total variation based noise removal algorithms

Reference 30

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Observation a7ef2c47-bbe7-46a8-8b54-e8a391fa0d39 · outbound

This paper cites Towards the first adversarially robust neural network model on MNIST.

A principled approach for generating adversarial images under non-smooth dissimilarity metrics Towards the first adversarially robust neural network model on MNIST

Reference 31

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A principled approach for generating adversarial images under non-smooth dissimilarity metrics One pixel attack for fooling deep neural networks

Reference 32

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A principled approach for generating adversarial images under non-smooth dissimilarity metrics Goodfellow, and Rob Fergus

Reference 33

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This paper cites Robustness May Be at Odds with Accuracy.

A principled approach for generating adversarial images under non-smooth dissimilarity metrics Robustness May Be at Odds with Accuracy

Reference 34

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This paper cites Spatially Transformed Adversarial Examples.

A principled approach for generating adversarial images under non-smooth dissimilarity metrics Spatially Transformed Adversarial Examples

Reference 35

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A principled approach for generating adversarial images under non-smooth dissimilarity metrics Aggregated Residual Transformations for Deep Neural Networks

Reference 36

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This paper cites Ad mm attack: An enhanced adversarial attack for deep neural networks with undetectable distortions.

A principled approach for generating adversarial images under non-smooth dissimilarity metrics Ad mm attack: An enhanced adversarial attack for deep neural networks with undetectable distortions

Reference 37

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A principled approach for generating adversarial images under non-smooth dissimilarity metrics Unresolved cited work

Reference 2016

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