Related Data for: Fingerprinting Deep Neural Networks - a DeepFool Approach (doi:10.21979/N9/ZDWQLI)

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Part 1: Document Description
Part 2: Study Description
Part 3: Data Files Description
Part 4: Variable Description
Part 5: Other Study-Related Materials
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Related Data for: Fingerprinting Deep Neural Networks - a DeepFool Approach

doi:10.21979/N9/ZDWQLI

DR-NTU (Data)

2024-10-16

1

Wang, Si; Chang, Chip Hong, 2024, "Related Data for: Fingerprinting Deep Neural Networks - a DeepFool Approach", https://doi.org/10.21979/N9/ZDWQLI, DR-NTU (Data), V1, UNF:6:SczDG/K0h5MoF1viJWM7Pg== [fileUNF]

Related Data for: Fingerprinting Deep Neural Networks - a DeepFool Approach

doi:10.21979/N9/ZDWQLI

Wang, Si (Nanyang Technological University)

Chang, Chip Hong (Nanyang Technological University)

Visual Studio Code

CHFA-GC1- AW01

DR-NTU (Data)

Wang, Si

Wang, Si

2024-07-24

https://doi.org/10.21979/N9/ZDWQLI

Engineering, Engineering, deep learning ip protection

This dataset contains partial program source code and the data for analysis for the paper "Fingerprinting Deep Neural Networks - a DeepFool Approach"

program source code, experimental data, process-produced data

10.1109/ISCAS51556.2021.9401119

Wang, S., & Chang, C. H. (2021, May). Fingerprinting deep neural networks-a deepfool approach. In 2021 IEEE International Symposium on Circuits and Systems (ISCAS) (pp. 1-5). IEEE.

10356/147023

Wang, S., & Chang, C. H. (2021, May). Fingerprinting deep neural networks-a deepfool approach. In 2021 IEEE International Symposium on Circuits and Systems (ISCAS) (pp. 1-5). IEEE.

• Number of cases: 46

• No. of variables per record: 9

• Type of File: text/tab-separated-values

UNF:6:SczDG/K0h5MoF1viJWM7Pg==

• ID - ID
• Dataset - Dataset
• model - model
• [0.3,1] - [0.3,1]
• (0.25,0.3] - (0.25,0.3]
• (0.2,0.25] - (0.2,0.25]
• (0.15,0.2] - (0.15,0.2]
• [0,15] - [0,15]
• Pirated? - Pirated?

f144027 Location:

Summary Statistics: Max. 45.0; Min. 0.0; Valid 46.0; Mean 22.5; StDev 13.42261772780059;

Variable Format: numeric

Notes: UNF:6:6asxlZYV4FZEamw6Rgo/cg==

f144027 Location:

Variable Format: character

Notes: UNF:6:1gUj4VhW/B7HvoFH13Wm7g==

f144027 Location:

Variable Format: character

Notes: UNF:6:iUd0eXLH5wXHvPteSPZeRw==

f144027 Location:

Summary Statistics: Min. 0.0; StDev 44.61231822263968; Mean 44.91304347826087; Valid 46.0; Max. 100.0;

Variable Format: numeric

Notes: UNF:6:bplFN/KZHOX5k2/zUFL7mw==

f144027 Location:

Summary Statistics: Mean 7.152173913043479; Valid 46.0; Max. 39.0; Min. 0.0; StDev 11.964702515323696;

Variable Format: numeric

Notes: UNF:6:CbveF79woPjH6CDnttNzOA==

f144027 Location:

Summary Statistics: Valid 46.0; Min. 0.0; StDev 8.512678807701592; Mean 5.391304347826087; Max. 39.0

Variable Format: numeric

Notes: UNF:6:pVA0qQArTm3ISB5ROF/ycQ==

f144027 Location:

Summary Statistics: Min. 0.0; StDev 19.227006469489154; Mean 13.5; Valid 46.0; Max. 53.0;

Variable Format: numeric

Notes: UNF:6:aU/P/l0QOel/yWEaj1VTkg==

f144027 Location:

Summary Statistics: Valid 46.0; Max. 100.0; Min. 0.0; StDev 36.99426773718637; Mean 29.043478260869566

Variable Format: numeric

Notes: UNF:6:ngDOo8022V5qePkLp4YEfg==

f144027 Location:

Variable Format: character

Notes: UNF:6:bZWci4D0lHMormi+M0Ng5w==

Label:

cifar100-resnet20-8412cc70.pth

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cifar100-resnet32-6568a0a0.pth

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cifar100-resnet44-20aaa8cf.pth

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cifar100-resnet56-2f147f26.pth

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cifar10-resnet20-30abc31d.pth

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cifar10-resnet32-e96f90cf.pth

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cifar10-resnet44-f2c66da5.pth

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cifar10-resnet56-f5939a66.pth

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deepfool.py

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fe1_checkpoint_0_29.pth

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fe1_checkpoint_0_29.pth

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fe1_model_0_29.pth

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fe1_model_0_29.pth

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flowchart.PNG

image/png

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fp1_checkpoint_0_14.pth

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fp1_checkpoint_0_19.pth

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fp1_model_0_14.pth

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fp1_model_0_19.pth

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fp2_checkpoint_0_14.pth

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fp2_checkpoint_0_19.pth

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fp2_model_0_14.pth

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fp2_model_0_19.pth

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fp3_checkpoint_0_14.pth

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fp3_model_0_14.pth

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ft_checkpoint_0_29.pth

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ft_checkpoint_0_29.pth

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ft_model_0_29.pth

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ft_model_0_29.pth

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gen_deepfool_imagenet10_resnet18.py

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hubconf.py

text/x-python

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__init__.cpython-38.pyc

application/octet-stream

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__init__.py

text/x-python

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load_img_tensor_imagenet10.py

text/x-python

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mr_comp_test.py

text/x-python

Label:

README.md

text/markdown

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readme.txt

text/plain

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resnet18_checkpoint_0_29.pth

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resnet18fe1_checkpoint_0_29.pth

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resnet18fe1_model_0_29.pth

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resnet18ft_checkpoint_0_29.pth

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resnet18ft_model_0_29.pth

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resnet18_model_0_29.pth

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resnet18wp1_checkpoint_0_29.pth

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resnet18wp1_model_0_29.pth

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resnet18wp2_checkpoint_0_29.pth

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resnet18wp2_model_0_29.pth

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resnet18wp3_checkpoint_0_29.pth

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resnet18wp3_model_0_29.pth

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resnet18wp4_checkpoint_0_29.pth

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resnet18wp4_model_0_29.pth

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resnet18wp5_checkpoint_0_29.pth

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resnet18wp5_model_0_29.pth

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resnet.cpython-38.pyc

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resnet.py

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setup.py

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wp1_checkpoint_0_19.pth

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wp1_checkpoint_0_29.pth

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wp1_model_0_19.pth

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wp1_model_0_29.pth

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wp2_checkpoint_0_19.pth

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wp2_checkpoint_0_19.pth

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wp2_model_0_19.pth

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wp2_model_0_19.pth

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wp3_checkpoint_0_14.pth

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wp3_checkpoint_0_19.pth

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wp3_model_0_14.pth

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wp3_model_0_19.pth

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wp4_checkpoint_0_14.pth

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wp4_checkpoint_0_19.pth

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wp4_model_0_14.pth

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wp4_model_0_19.pth

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wp5_checkpoint_0_14.pth

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wp5_checkpoint_0_19.pth

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wp5_model_0_14.pth

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wp5_model_0_19.pth

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wp6_checkpoint_0_14.pth

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wp6_checkpoint_0_19.pth

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wp6_model_0_14.pth

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wp6_model_0_19.pth

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