Paper Citation Record · LEDGER

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation

As of 24 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2411.16207.

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

pith.paper-citation-record.v1
2411.16207 v2

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:31:16.723569Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

42 of 42 outbound references displayed

verified exact0
verified fuzzy37
unresolved5
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5a45f439-b956-4db3-b3bd-7bef86a91f84 · outbound

This paper cites Gradient-based learning applied to document recognition,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Gradient-based learning applied to document recognition,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T13:31:16.572861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed0ef74c-d760-4a20-9f21-1c12694b8b1b · outbound

This paper cites Imagenet classification with deep convolutional neural networks,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Imagenet classification with deep convolutional neural networks,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:17.196733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation dc216540-43f0-44cb-b708-addabcebb01c · outbound

This paper cites Very deep convolutional networks for large-scale image recognition,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Very deep convolutional networks for large-scale image recognition,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:17.186067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b6677772-4ea6-464c-bef7-5815cebbfb97 · outbound

This paper cites Deep residual learning for image recognition,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Deep residual learning for image recognition,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:17.174955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 672cbd7b-df75-4b63-a029-e71d1de475e5 · outbound

This paper cites An image is worth 16x16 words: Trans- formers for image recognition at scale,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation An image is worth 16x16 words: Trans- formers for image recognition at scale,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:17.163367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 69f5dad3-acc7-420d-b1be-af6259c27791 · outbound

This paper cites Attention is all you need,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Attention is all you need,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T13:31:16.593859Z

Source-reported events for the cited work

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Observation f1b1d456-dde8-451b-86d1-6828f2de0ea7 · outbound

This paper cites End-to-end object detection with transformers,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation End-to-end object detection with transformers,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:17.145557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a4fffdd7-0bb9-4306-8602-2b00e7e75765 · outbound

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

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:17.134950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 46f252b5-b684-4d14-896f-3f8550c794db · outbound

This paper cites Estimating information from image colors: An application to digital cameras and natural scenes,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Estimating information from image colors: An application to digital cameras and natural scenes,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:17.123464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 811ef336-9149-4e79-93b5-08f380970a2f · outbound

This paper cites From global to local: Multi-patch and multi-scale contrastive similarity learning for unsupervised defocus blur detection,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation From global to local: Multi-patch and multi-scale contrastive similarity learning for unsupervised defocus blur detection,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:17.113275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation eea9fa2a-ef3a-4782-bc35-39e0dfb6ba57 · outbound

This paper cites Domain adaptation for underwater image enhancement,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Domain adaptation for underwater image enhancement,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:17.100987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1c3c0d78-1939-438b-a31e-372d3d766b20 · outbound

This paper cites CONVIQT: contrastive video quality estimator,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation CONVIQT: contrastive video quality estimator,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:17.090273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5fd81e26-3147-441a-84c5-3dfd832663fd · outbound

This paper cites Doing more with moir´e pattern detection in digital photos,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Doing more with moir´e pattern detection in digital photos,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:17.077987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a1076e73-52ba-4241-8d9c-d1ecbc9a58bb · outbound

This paper cites Sharpformer: Learning local feature preserving global representations for image deblurring,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Sharpformer: Learning local feature preserving global representations for image deblurring,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:17.065799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 41804092-c6b6-42ee-b085-d766a5e5bdec · outbound

This paper cites Lossless recompression of JPEG images using transform domain intra prediction,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Lossless recompression of JPEG images using transform domain intra prediction,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:17.053841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 22505f0b-bce9-468b-a858-610aaec3bf38 · outbound

This paper cites Secure outsourced SIFT: accurate and efficient privacy-preserving image SIFT feature extraction,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Secure outsourced SIFT: accurate and efficient privacy-preserving image SIFT feature extraction,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:17.041437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bde06154-dabf-4b64-90a7-be250b127b6f · outbound

This paper cites Applied cryptography: Protocols, algorthms, and source code in c.-2nd,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Applied cryptography: Protocols, algorthms, and source code in c.-2nd,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:17.030569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ab988561-b645-4c8b-9511-1a024baf111c · outbound

This paper cites Schneier, Applied cryptography: protocols, algorithms, and source code in C.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Schneier, Applied cryptography: protocols, algorithms, and source code in C

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:17.018843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2df8360b-0da7-4414-b4a1-fe2fdf217c37 · outbound

This paper cites Synchronization in chaotic systems,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Synchronization in chaotic systems,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T13:31:16.639958Z

Source-reported events for the cited work

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Observation d00f6fac-8f06-44ed-b2a7-a776a05d893c · outbound

This paper cites Reliable detection of LSB steganography in color and grayscale images,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Reliable detection of LSB steganography in color and grayscale images,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:17.000564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 40f9e7d6-e52d-48a2-86c1-500a1c26f83c · outbound

This paper cites Deepedn: A deep-learning-based image encryption and decryption network for internet of medical things,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Deepedn: A deep-learning-based image encryption and decryption network for internet of medical things,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:16.990046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 365c62cb-cffa-4e57-8d15-3d9c5123d17b · outbound

This paper cites Generating any number of initial offset-boosted coexisting chua’s double-scroll at- tractors via piecewise-nonlinear memristor,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Generating any number of initial offset-boosted coexisting chua’s double-scroll at- tractors via piecewise-nonlinear memristor,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:16.979214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2d80e08e-5e11-47d6-bc9a-64813f308594 · outbound

This paper cites Grayscale and colored image encryption model using a novel fused magic cube,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Grayscale and colored image encryption model using a novel fused magic cube,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:16.967664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 18b8137a-be1e-415a-9278-6bd4f0861bd7 · outbound

This paper cites ANN for time series under the fr ´echet distance,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation ANN for time series under the fr ´echet distance,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:16.956739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 79a48d44-5b45-4c2a-8413-b49ed4bda0b4 · outbound

This paper cites Tight bounds for approximate near neighbor searching for time series under the fr´echet distance,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Tight bounds for approximate near neighbor searching for time series under the fr´echet distance,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:16.945212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 64cab99d-1bc6-47d0-9d30-a714b18801d6 · outbound

This paper cites Curve simplification and clustering under fr´echet distance,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Curve simplification and clustering under fr´echet distance,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:16.933909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3a38e9cf-058a-4176-b042-a70d13a496d8 · outbound

This paper cites Crafting training degradation distribution for the accuracy-generalization trade-off in real-world super-resolution,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Crafting training degradation distribution for the accuracy-generalization trade-off in real-world super-resolution,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:16.923277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c47411d1-3159-4369-8b54-e2e0b970937e · outbound

This paper cites On aliased resizing and surprising subtleties in GAN evaluation,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation On aliased resizing and surprising subtleties in GAN evaluation,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:16.911962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0ff47eb9-619a-4067-aa61-5fdd81ac914d · outbound

This paper cites Visual DNA: representing and comparing images using distributions of neuron ac- tivations,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Visual DNA: representing and comparing images using distributions of neuron ac- tivations,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:16.900578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 12bcd51a-46cb-411d-9c12-f17ebfe8eff5 · outbound

This paper cites Color image encryption and authentication using dynamic DNA encoding and hyper chaotic system,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Color image encryption and authentication using dynamic DNA encoding and hyper chaotic system,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:16.889807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b3008203-8a9b-4096-9461-a578a6afa469 · outbound

This paper cites A new fractional-order chaos system of hopfield neural network and its application in image encryption,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation A new fractional-order chaos system of hopfield neural network and its application in image encryption,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:16.876856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cdf49778-fc48-4f4f-bfcf-32bac226fc4d · outbound

This paper cites Novel image encryption scheme based on chaotic signals with finite-precision error,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Novel image encryption scheme based on chaotic signals with finite-precision error,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:16.865236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3b4fe18e-2829-4879-9218-afdec0dd7840 · outbound

This paper cites On data banks and privacy homomorphisms,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation On data banks and privacy homomorphisms,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:16.854043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fddabfe3-14af-4785-a189-34512aa5d6aa · outbound

This paper cites Gentry, A fully homomorphic encryption scheme , 2009.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Gentry, A fully homomorphic encryption scheme , 2009

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:16.843208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bcf54628-f689-4998-9388-cdb4521fb726 · outbound

This paper cites Learnable privacy-preserving anonymiza- tion for pedestrian images,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Learnable privacy-preserving anonymiza- tion for pedestrian images,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:16.831771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b3d0872a-e8ca-4b08-92c7-986cea6a01d1 · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-12T13:31:16.701597Z

Source-reported events for the cited work

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Observation de521747-1085-40d9-ae38-42bb5d8dae79 · outbound

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

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Learning multiple layers of features from tiny images,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-12T13:31:16.705732Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fae9c235-8c02-4a4d-95d5-78bf1eac3677 · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Communication-efficient learning of deep networks from decentralized data,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:16.812269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 19a37147-3672-4b53-ac58-f86505c736dd · outbound

This paper cites Deep leakage from gradients,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Deep leakage from gradients,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:16.801365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation aef43129-fe00-4224-bd49-427addab47e4 · outbound

This paper cites Automatic transformation search against deep leakage from gradients,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Automatic transformation search against deep leakage from gradients,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:16.789817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2871eb9c-2cfc-4152-b8e0-232ce7fa1c79 · outbound

This paper cites Using highly compressed gradients in federated learning for data reconstruction attacks,.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation Using highly compressed gradients in federated learning for data reconstruction attacks,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:16.778061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cd01b57e-37c8-4450-8b95-89ee71d6bbbd · outbound

This paper cites He has authored or coauthored more than 120 research papers in international conferences and journals.

Can Encrypted Images Still Train Neural Networks? Investigating Image Information and Random Vortex Transformation He has authored or coauthored more than 120 research papers in international conferences and journals

Reference 2010

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:31:16.764782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

No inbound Pith citation observations are available.