The Prog Hub Blog

  The strength of deepfake detection depends on the quality of the training data. Deepfake detection methods run the risk of being ineffective or, worse, biased against particular populations in the absence of diverse, representative, and high-quality datasets. This chapter explores the ethical issues surrounding the use of open versus proprietary datasets, the difficulties in curating datasets for deepfake detection, and the function of data augmentation in enhancing model resilience. Managing Superior Deepfake Datasets: Diversity and Representativeness Concerns Any deepfake detection system must be trained on a high-quality, diverse, and well-labeled dataset in order to function properly. Curating such a dataset, however, comes with a number of difficulties, such as: Issues with Data Scarcity and Quality 1. Lack of Realistic Deepfake Samples:  The majority of datasets are less reflective of the most recent AI-generated manipulations because they contain deepfakes produced wi...

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Based on the free open-source code for bitcoin, thousands of variants are available. Some call themselves better than bitcoin, although that may not be the case. Bitcoin is certainly a trendsetter for other cryptocurrencies. Altcoins have the same framework as that of bitcoin. A few other more-known digital currencies after bitcoin are Bitcoin Cash, Dash, Ethereum, IOTA, Litecoin, Monero, NEM, Ripple, and Zcash. The transaction speed, transaction fee, unit price, active community, acceptance by users and retailers, and so on, are some of the various factors based on which the digital currencies make in the list. Despite of the many current and upcoming digital currencies, bitcoin is still maintaining a great hold as a leader due to its acceptance, popularity, and market capitalization. Ethereum and Ripple are more accepted by the corporations and have their major backing. Altcoins try to capture market by presenting themselves with some objective. These objectives are not just ...