Veritus is name of the tool we developed to tackle the problem of fake-news dissipation and disinformation during the COVID-19 pandemic. This tool has two pillars: Image Analysis Image manipulation is the technique of re-shaping digital content to portray a skewed version of the actual image. There are undoubtedly many benefits to altering media in this way, particularly in the movie industry, but ‘fake’ content can have direct consequences for those it misrepresents. The rapid growth of technology in domains such as social connectivity and information accessibility makes spreading the information, along with disinformation, be it via the medium of text or images very unchallenging. With innumerable software that facilitate streamlined manipulation of images, it becomes trickier by the day as to assess the authenticity of the image. With the spread of forged images on social media, aimed at fostering controversy and hostility, digital image forensics to test the originality of the image can extremely helpful. Typically, digital image forensics is a field of study identifying the origin and verifying the authenticity of the image. As numerous false images flood the internet and social media platforms, the need for a tool to help an individual determine its authenticity can be looked upon as an asset. Numerous models and techniques are used to determine the level of authenticity of an image, ones that determine the quality of the image compression level results. In this research, the methods used to measure the level of compression is done via Error Level Analysis (ELA). Error Level Analysis (ELA) is a forensic technique applied on the image to analyse image through different levels of compression. This technique is used to detect digitally modified images. Veritus works for both mainly shared image formats i.e. • .png (Portable Network Graphics) • .jpeg (Joint Photographic Experts Group) Text Analysis As an increasing portion of our lives is spent interacting online through social media platforms, more and more people tend to seek out and consume news from social media rather than traditional news organisations. The reasons for this change in consumption behaviour is inherent in the nature of these social media platforms: 1. It is often more timely and less expensive to consume news on social media compared with traditional news media, such as newspapers or television. 2. It is easier to further share, comment on, and discuss the news with friends or other readers on social media. Despite the advantages provided by social media, the quality and credibility of news on social media is lower than traditional news houses. Due to the inexpensive (almost free) set up costs of social media presence, malicious content distributors can easily set up digital shops and disrupt circulation of authentic news. This coupled with the exponential spreading of news with the share feature makes the dissipation of the news, authentic and fake alike extremely fast. During the pandemic situation of COVID-19 many fake news were also fatal to many and destroyed many lives. The main motive of Veritus is to stop this. Developing the text analysis feature Veritus was a bit challenging as a model had to be trained to classify fake news from the authentic one. The model was trained with a dataset of fake news that had to manually collected and some was already available. The training dataset included equal number of true and False news so that we could avoid data redundancy during the training of the model. To evaluate the performance of algorithms for fake news detection, various evaluation metrics have been used. In this subsection, we review the most widely used metrics for fake news detection. Most existing approaches consider the fake news problem as a classification problem that predicts whether a news article is fake or not: • True Positive (TP): when predicted fake news pieces are actually annotated as fake news; • True Negative (TN): when predicted true news pieces are actually annotated as true news; • False Negative (FN): when predicted true news pieces are actually annotated as fake news; • False Positive (FP): when predicted fake news pieces are actually annotated as true news.