1. [1] AMERINI, I., ANAGNOSTOPOULOS, A., MAIANO, L., CELSI, L.R., 2021. Deep learning for multimedia forensics Foundations and Trends in Computer Graphics and Vision. ISSN 15722740, DOI 10.1561/0600000096.10.1561/9781680838558
2. [2] BHATT, P.M., MALHAN, R.K., RAJENDRAN, P., SHAH, B.C., THAKAR, S., YOON, Y.J., GUPTA, S.K, 2021. Image-Based Surface Defect Detection Using Deep Learning: A Review (2021) Journal of Computing and Information Science in Engineering, 21(4), art. No. 4049535. Cited 2 times. 1) https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101153445&doi=10.1115%2f1.4049535∂nerID=40&md5=a5213a78218aeb49275b303a6797f40b, DOI 10.1115/1.4049535.
3. [3] KHAN, M.A., MITTAL, M., GOYAL, L.M., ROY, S., 2021. A deep survey on supervised learning based human detection and activity classification methods. Multimedia Tools and Applications, 80(18), pp. 27867-27923. Cited 3 times. 1) https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108802610&doi=10.1007%2fs11042-021-10811-5∂nerID=40&md5=cee3db6c37193a7a2a94d91aa4295bd1, DOI: 10.1007/s11042-021-10811-5.10.1007/s11042-021-10811-5
4. [4] GUAN, K.M., ANDERSON, T.I., CREUX, P., KOVSCEK, A.R., 2021. Reconstructing porous media using generative flow networks (2021) Computers and Geosciences, 156, art. No. 104905, . 1) https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112298638&doi=10.1016%2fj.cageo.2021.104905∂nerID=40&md5=5897ecfe28548319f8242a9c8a1d5daf, DOI: 10.1016/j.cageo.2021.104905.10.1016/j.cageo.2021.104905
5. [5] RAVEENDRAN, S., PATIL, M.D., BIRAJDAR, G.K., 2021. Underwater image enhancement: a comprehensive review, recent trends, challenges and applications. Artificial Intelligence Review, 54(7), pp. 5413-5467. 1) https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107324533&doi=10.1007%2fs10462-021-10025-z∂nerID=40&md5=979366750f1ddeedeac28caeea8583cf, DOI: 10.1007/s10462-021-10025-z.10.1007/s10462-021-10025-z