7/21/2023 0 Comments Cnn in panic modeHence, to solve this problem, the United States Defense Advanced Research Projects Agency (DARPA) launched a media forensics research plan to develop fake digital media detection methods 5. Since most are also based on deep learning, a conflict between bad and good deep learning applications has developed 4. Many different strategies have been put out so far to find deep fakes. Dealing with deep fakes is significantly more difficult because they are mostly utilized for harmful objectives and virtually anyone can now produce deep fakes utilizing the tools already available. The importance of discovering the truth in the digital realm has therefore increased. Last but not least, deep audio fakes or voice cloning is used to manipulate an individual's voice that associates something with the speaker they haven’t said in actual 1, 3. Using fictitious profiles, this is done to propagate false information on social media. With puppet-master, deep fakes are produced by imitating the target's facial expressions, eye movements, and head movements. The purpose of lip-syncing is to simulate the victim's attacker's voice by having someone talk in that voice. In another type of deep fake called lip-synching, the target person’s lips are manipulated to alter the movements according to a certain audio track. In face-swap deep fakes, a person's face is swapped with that of the source person to create a fake video to target a person for the activities they have not committed 1, which can tarnish the reputation of the person 2. The following categories of deep fake videos exist: face-swap, synthesis, and manipulation of facial features. ![]() Especially when deep fake generation becomes more complex, this is anticipated to become a difficult task. The authenticity and integrity of any video submitted as evidence must be established. Using videos as evidence in legal disputes and criminal court cases is standard practice. Deep fakes are synthesized audio and video content generated via AI algorithms. Deep fake creation has evolved dramatically in recent years, and it might be used to spread disinformation worldwide, posing a serious threat soon. ![]() We now live in such times where spreading disinformation can be easily used to sway peoples’ opinions and can be used in election manipulation or defamation of any individual. At the same time, we have seen enormous progress in complex yet efficient machine learning (ML) and Deep Learning (DL) algorithms that can be deployed for manipulating audiovisual content to disseminate misinformation and damage the reputation of people online. The rise in social media applications has enabled people to quickly share this content across the platforms, drastically increasing online content, and providing easy access. In the last decade, social media content such as photographs and movies has grown exponentially online due to inexpensive devices such as smartphones, cameras, and computers. The results prove the efficiency and robustness of the proposed technique hence, it can be used to detect deep fake images and reduce the potential threat of slander and propaganda. The proposed method achieved the highest accuracy of 89.5% via Residual Network and K-Nearest Neighbor. The resultant feature vectors are then classified via Support Vector Machines and K-Nearest Neighbors by performing hyper-parameter optimization. This image is then supplied to Convolutional Neural Networks for deep feature extraction. The proposed framework initially performs an Error Level Analysis of the image to determine if the image has been modified. Hence, these problems can limit their usefulness in real-world applications where the data constantly evolves. Moreover, these systems are sensitive to noise or variations in the data, which can reduce their performance. These systems cannot generalize well to unseen data. ![]() Traditional Machine Learning (ML) based systems employing handcrafted feature extraction fail to capture more complex patterns that are poorly understood or easily represented using simple features. This paper proposes an automated method to classify deep fake images by employing Deep Learning and Machine Learning based methodologies. Hence, a robust system to differentiate between real and fake content has become crucial in this age of social media. This rapid advancement can cause panic and chaos as anyone can easily create propaganda using these technologies. Due to the wide availability of easy-to-access content on social media, along with the advanced tools and inexpensive computing infrastructure, has made it very easy for people to produce deep fakes that can cause to spread disinformation and hoaxes.
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