Abstract

ABSTRACT With the widespread adoption of social media, the dissemination of information has accelerated dramatically. However, this rapid spread poses challenges, as much of the content lacks verification, leading to the proliferation of fake news and manipulated visuals. Existing detection methods often emphasize text classification, overlooking visual forgeries. This study introduces FakeNews, a framework integrating a modified BERT model (FaketextBERT) for fake text detection and a modified CNN model (ForgeryCNN) for identifying forged images. Key contributions include broadening the fake news definition to include text and images, introducing modality-specific and integrated detection approaches, and achieving higher accuracy through Adam optimizer and ELA images. Experimental results demonstrate enhanced detection performance and improved recognition of fake news.

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