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DEEPFAKES: HOW AI HAS IMPROVED OR IS IT FREAKY?

Niti Sharma
Lenovo Many GEOs

AI has been a tremendous source of multitudinous growth in industries like healthcare, fashion, education, agriculture and many others. Every innovation comes packed with its good and bad impact. This powerful tool when put to its misuse can cause disruption in the entire gamut of things.  This is the era of doctored videos and the misuse of technology has caused quite a propaganda in the past years. Deepfake technology is a kind of key weapon that is now being used for all the wrong reasons. Deepfake constitute content that is fake in the form of media like videos and forms like pictures and audio.

They’re created by using artificial intelligence where data is fed on a computer to find a new face. AI trickery started as an entertainment bit on the social media platforms, which took wrong turn and went on exploiting the internet by storm. It’s a little intimidating when someone says that it’s all technology. That’s what scares people today. Faces of celebrities and politicians were morphed onto a different body, hence creating highly convincing fake videos with neural networks. The year 2020 saw Deepfake going all mainstream. A deepfake video surfacing in 2018 depicting Barak Obama calling Trump an expletive and many other manipulative stuffs severely jeopardize the role of AI consultants who labour hard to bring forth the best in technology and are also held accountable for the wrongs that emerge as a result.

Deepfakes and deep Learning models can also be used to create fake images. AI models have become really good at generating fake faces that look almost identical to real people & it’s impossible to distinguish between the real and the AI generated fake. Every technology can be put to a use that no one has ever imagined, but putting to a devastating use like this is going to escalate the negative effects. Threats posed by Deepfakes have been increasing with every passing day. Facts can be easily obfuscated and can enhance fabrication to highest levels.

TECHNOLOGY BEHIND FAKE IMAGES

Deepfakes and fake images are generated by a class of machine learning models called GANs- Generative Adversarial Networks, designed by researcher Ian Goodfellow and his colleagues back in 2014. It involved using two neural networks and having them compete against each other. The first called the generator and the latter as discriminator. The generator’s job was to trick the discriminator into believing the images created were real. As the discriminator got better at identifying fake images, the generator would start producing more realistic images to trick the discriminator. GAN is the most fascinating innovations in the field of deep learning and was vouched as the coolest idea in last 2 decades.

REAL Vs GAN GENERATED IMAGES

Generative Adversarial Networks (GAN) are algorithmic architectures that use two neural networks, pitting one against the other in order to generate new synthetic instances of data that can pass for real data. They’re widely used in image generation, video generation and voice generation. GAN model is data-hungry and rely heavily on vast quantities and high-quality training examples in order to generate high fidelity natural images of diverse categories.

As mentioned earlier, GAN generated images are very convincing and proves to be dangerous as GANs can be used to create fake dating profiles, catfish people and spread fake information. It becomes imperative to be able to distinguish between fake and real. Here comes the role of a trained AI engineer who dons one of the top AI certifications that could lead them to evaluate, investigate and discern a quintessential conclusion. The AI courses market is flooded with options from the international certifications to many seasoned players in the industry. The question arises Who is eligible to do the AI certification? This area is not programming-oriented fields, so yes, people who have no programming experience can also learn AI and Machine Learning. People with computer science background will have an added advantage but that’s not the only prerequisite.

The Certified Artificial Intelligence Engineer (CAIE) from USAII is the most sought-after credential that sets you apart from your peer as it leads you to jumpstart AI career. There are other competitors from Coursera, edX and others that also provide such AI certifications. So, when you think of getting into a field that is ever-evolving and is one of the promising industry roles to vouch for, why not learn from the best in the industry like USAII.

When talking about an incredibly clever innovation like GAN, you just probably need to get better on guessing whether the posed picture is real or fake. As you keep practicing, you’ll get better at identifying computer-generated images. Eventually what you’ll be doing is exactly the same thing a discriminator does in GAN. It learns the difference between fake and real images. Overtime, it gets better at identifying the fake ones.

WHY GANs?

Data augmentation results in better performing models, both increasing model skill and providing a regularizing effect, reducing generalization error. It works by creating new, artificial but plausible examples from the input problem domain on which the model is trained. Successful generative modelling provides an alternative and potentially more domain-specific approach for data augmentation. In fact, it is a simplified version of generative modelling.

Perhaps the most compelling application of GANs is in conditional GANs for tasks that require the generation of new examples. Goodfellow indicates three main examples:

  • Image super-resolution (ability to generate high-resolution versions of input images)
  • Creating art (ability to create great new artistic images, sketches and the like)
  • Image-to-image translation (ability to transform photographs across domains)

Astonishing is not a sufficient adjective for their capability and success. If these insanely helpful technologies are put to right use then we can surely promise a wider discovery of even-better and mean AI tools in future. Hence, the importance of upskilling and arming oneself with the top AI certifications so as to be the best fit for this ever-evolving and promising AI industry.


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