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This research explores different detection
techniques used to segregate content generated by humans
and AI. The prevalence of fake news and manipulated images
or videos has become a significant issue in the internet and
social media age. A dataset of text and image samples
generated by AI and human sources was collected and
preprocessed. Two feature extraction methods, TF-IDF for text
samples and LBP for image samples, were used in this study,
and several machine learning algorithms, such as Decision
Tree, Random Forest, and Support Vector Machine, were
trained on the extracted features. The performance of each
algorithm was evaluated using metrics such as accuracy,
precision, recall, and F1 score. The study concludes that these
techniques can effectively detect fake content generated by
humans and AI.
Keywords:
Fake Images, AI Detection, Fake Content, Fake News, Segregation
Cite Article:
"Different Detection Techniques Used to Segregate the Content Generated From AI Or Human", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 4, page no.488 - 491, April-2023, Available :http://www.ijrti.org/papers/IJRTI2304080.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator