IEEE Standard for Adoption of Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI) Technical Specification Neural Network Watermarking (NNW) V1
IEEE Std 3304-2023
| contributor author | IEEE - The Institute of Electrical and Electronics Engineers, Inc. | |
| date accessioned | 2024-12-17T08:46:51Z | |
| date available | 2024-12-17T08:46:51Z | |
| date copyright | 12 February 2024 | |
| date issued | 2024 | |
| identifier other | 10431696.pdf | |
| identifier uri | http://yse.yabesh.ir/std;jsessioutho162s93F7081D544/handle/yse/336431 | |
| description abstract | This is an adoption of the Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI)--Technical Specification Neural Network Watermarking as an IEEE Standard. The Neural Network Watermarking (MPAI-NNW) Technical Specification provides standard methods to measure the ability of 1) a watermark inserter to inject a payload without deteriorating the neural network (NN) performance, 2) a watermark detector to recognize the presence and the watermark decoder to successfully retrieve the payload of the inserted watermark, and 3) a watermark inserter to inject a payload and the watermark detector/decoder to detect/decode a payload from a watermarked model or any of its inferences at a measured computational cost. | |
| language | English | |
| publisher | IEEE - The Institute of Electrical and Electronics Engineers, Inc. | |
| title | IEEE Standard for Adoption of Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI) Technical Specification Neural Network Watermarking (NNW) V1 | en |
| title | IEEE Std 3304-2023 | num |
| type | standard | |
| page | 26 | |
| tree | IEEE - The Institute of Electrical and Electronics Engineers, Inc.:;2024 | |
| contenttype | fulltext | |
| subject keywords | Neural Network Watermarking | |
| subject keywords | AI framework | |
| subject keywords | MPAI-NNW | |
| subject keywords | IEEE 3304™ | |
| subject keywords | Artificial Intelligence | |
| subject keywords | adoption | |
| identifier DOI | 10.1109/IEEESTD.2024.10431696 |

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