Created
June 15, 2021 05:57
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a python script to search publication record based on publication names
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| from scholarly import scholarly | |
| listpubs = """ | |
| Embedding watermarks into deep neural networks | |
| Turning your weakness into a strength: Watermarking deep neural networks by backdooring | |
| DeepMarks: A Digital Fingerprinting Framework for Deep Neural Networks | |
| Protecting intellectual property of deep neural networks with watermarking | |
| DeepSigns: A Generic Watermark- ing Framework for IP Protection of Deep Learning Models | |
| Adversarial Frontier Stitching for Remote Neural Network Watermarking | |
| Watermarking deep neural networks for embedded systems | |
| Zero-knowledge watermark detection resistant to ambiguity attacks | |
| Combatting ambiguity attacks via selective detection of embedded watermarks | |
| Passport-aware normalization for deep model protection | |
| Robust image watermarking based on multiscale gradient direction quantization | |
| Screen-shooting resilient watermarking | |
| An overview of digital video watermarking | |
| Training dnn model with secret key for model protection | |
| Keynet: An asymmetric key-style framework for watermarking deep learning models | |
| A survey on model watermarking neural networks | |
| Identity Bracelets for deep neural network | |
| """.strip().split('\n') | |
| res = [] | |
| for pubname in listpubs: | |
| print(f'Searching: {pubname}') | |
| search_query = scholarly.search_pubs(pubname) | |
| res.append({ | |
| 'pub': pubname, | |
| 'res': next(search_query) | |
| }) | |
| print(res[-1]['res']) | |
| def get_scholar_url(author_id): | |
| if author_id == '': | |
| return 'no scholar url' | |
| scholar_url = f'https://scholar.google.com/citations?user={author_id}&hl=en' | |
| return scholar_url | |
| df = [] | |
| lines = [] | |
| maxlen_authors = 0 | |
| for pub, query in zip(listpubs, res): | |
| print(pub) | |
| authors = query['res']['bib']['author'] | |
| author_ids = query['res']['author_id'] | |
| venue = query['res']['bib']['venue'] | |
| year = query['res']['bib']['pub_year'] | |
| citations = query['res']['num_citations'] | |
| pub_url = query['res']['pub_url'] | |
| eprint_url = query['res']['eprint_url'] | |
| authors_summary = [] | |
| author_infos = [] | |
| for author, author_id in zip(authors, author_ids): | |
| scholar_url = get_scholar_url(author_id) | |
| authors_summary.append({ | |
| 'author': author, | |
| 'author_id': author_id, | |
| 'scholar_url': scholar_url | |
| }) | |
| author_infos.append(f'{author},{scholar_url}') | |
| author_infos = ','.join(author_infos) | |
| maxlen_authors = max(maxlen_authors, len(authors)) | |
| df.append({ | |
| 'pub': pub, | |
| 'venue': venue, | |
| 'year': year, | |
| 'num_cites': citations, | |
| 'pub_url': pub_url, | |
| 'eprint_url': eprint_url | |
| }) | |
| lines.append(f'{pub},{venue},{year},{citations},{pub_url},{eprint_url},{author_infos}') | |
| print(lines[-1]) | |
| # print(authors, author_id) | |
| commas = ',' * maxlen_authors * 2 | |
| with open('ipr list.csv', 'w+') as f: | |
| f.write(f'Pub Name,Venue,Year,Citations,Pub Url,Eprint Url{commas}\n') | |
| for line in lines: | |
| f.write(line) | |
| f.write('\n') |
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