The latest development using Artificial intelligence – Deep fake, relies on machine learning algorithms to create fake sounds, pictures, and videos. These artificial media are often used for propaganda or deception and can depict global leaders or celebrities speaking untrue information. They can be hard to detect when executed skillfully. Threatening public confidence in the truth, spreading false information, and harming reputations. It is one of the latest precedents of AI endangering cyber security.
Cybercriminals can use deepfakes as weapons to topple governments and corporations. The popularity of deep fake videos has sparked concerns about technological abuse due to its rapid development.
How Is AI Involved In The Making Of Deep Fake? AI Endangering Cyber Security
Has benefited many industries. But now, there is a rise in cases of AI endangering cyber security. Deep fake is made by using AI. Two algorithms are mainly used in the generation of fake content: a discriminator and a generator. The discriminator determines the originality of the version used. The generator produces a training data set for the misleading content. The discriminator finds errors that the generator can fix, and the generator gets better at producing realistic output.
A Generative Adversarial Network (GAN) is produced by combining these techniques. GANs employ deep learning to identify patterns in actual pictures, which they then use to produce fakes. When detecting deep fakes, a GAN looks at images of the target taken from different viewpoints to gather all the information. When it comes to deep fakes, the GAN examines voice, movement, and behavior patterns. Then, it passes the data through the discriminator several times to adjust the final picture or video’s realism.
Source video deep fakes use a neural network-based autoencoder to examine the footage. It comprehends pertinent target characteristics like body language and facial expressions to construct deep fakes.
In audio deep fakes, a model is built using speech patterns, and the audio of a person’s voice is replicated.
Another popular method for creating realistic deep fakes is lip-syncing. It involves projecting a vocal recording onto the screen to offer the impression that the subject is speaking the lines.
Ways AI Is Endangering Cyber Security
AI has benefited many industries. But, there are often consequences of technological innovation, such as AI endangering cyber security. Among the instances is the development of deep fakes. It has led to the rise of cyber fraud and image forgery, particularly on social media platforms.
Threat To Individuals
Deep learning algorithms, such as GANs and variational auto-encoders, are used to create fake images and videos. These outputs easily go viral on social media sites. However, they pose a serious concern to cybersecurity. Threatening personal privacy and social image.
Also, the algorithms used can pose a threat to cybersecurity by enabling text analysis, sentence structuring, and understanding human language. The development of these algorithms requires extensive training and can lead to the execution of various crimes. Therefore, it is crucial to manage the misuse of technology to protect social and ethical values.
Threat To Organizations
Hoaxes and frauds using deep fake technology can undermine and destabilize organizations. Cybercriminals can fabricate stories and hoaxes, harming a company’s social image and share price as well.
Financial fraud and identity theft are two more possible applications of deep fake technology. Perpetrators can fabricate papers or mimic the voice of their target. In general, deep fake technology seriously jeopardizes privacy and public safety. An example of AI endangering cyber security.
These skillfully constructed audio and video forgeries accurately imitate actual individuals, making it hard to distinguish from reality. An emerging cyber threat that calls for creative countermeasures.
Phishers can create fake videos, voice messages, and social engineering attacks to compromise login credentials and gain access to personal data. Machine learning algorithms that can imitate human language and execute commands also pose a threat. To protect against cybercrime, individuals and organizations must exercise caution, implement security controls, and regularly update countermeasure technology.
