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Deepfake Threats That Challenges Technology

Deepfake Threats That Challenges Technology

Deepfake Threats That Challenges Technology

Technology makes it more challenging to know whether any of the news you see and hear on the internet is true. Deepfake videos are not new in the cybersecurity landscape. Not until recently, they were not seen as a large risk. Cybercriminals can now create compelling false audios and videos.  That is due to modern innovations like Machine Learning and Artificial Intelligence.
 
One of the biggest threats is that hackers fine-tune the approach. They gain from it further when they know how things properly work. Organizations are already struggling to deal with advanced email phishing attacks. The increasing threat of deepfake videos will become a more complex challenge to address. Widespread deepfakes may not be the trend right now. Yet, for hackers who have the resources and ability to make this tactic work for them, they can opt for deepfakes as a favorite weapon.
 
Most cybersecurity experts respond with more technologies to problems posed by cybercriminals. In this scenario, though, technology does not do the goal of countering deepfake. It is a challenge that will be tough for technology to overcome.
 

Deepfake Challenges That Technology Cannot Easily Solve

 
The rise of deepfake videos is now putting cybersecurity experts and enthusiasts at the edge of their seats. It has posed a new challenge in detecting and resolving the issues. Common cybersecurity threats like malware, ransomware, and other viruses are easily blocked. Installing reliable antivirus software like Bitdefender Antivirus Plus, which you can easily find in online software stores, can protect your business from hackers.
 
Yet, with deepfake videos, technology finds it hard to solve this quickly. Here are some of the challenges that technology faces in solving this cybersecurity threat:
 
  • Proving What Is Real Or Not

 
Technology cannot quickly discern whether a circulated video is genuine. As per the newly released study of Britt Paris, technical models have a tough time analyzing fake video quality. Tech firm giants such as Facebook and Google have concentrated on creating tools to expose deepfake. Such software algorithms for detection training systems and watermarks incorporates into digital picture files to show distortion.
 
Several developers have also been working on address the issue. They are using apps that validates captured images and videos. That provides a basis for evaluation if later, a leak happens with the copies of the material. As Paris points out, though, it would be difficult for technologies to connect bogus videos with technical methods for capturing deepfakes.
 
Tech firms still need to hire human content editors these days. Media organizations need to train more detection and verification journalists that can also act as fact-checkers. These ground reports will establish whether a video represents a fact or not.
 
  • Detected Too Late

 
A deep fake detector may not have helped with such a judgment call. The technology to identify whether a video is false or not cannot correctly determine whether it is. One answer to the underlying problem is to grant more authority to those capable of making decision calls.  These comprises of human content administrators. To that end, as an integral part of ensuring a secure internet, moderators should recieve bigger compesation. They should also educated further, and have better appreciation.
 
  • Deepfake Detectors Are Not Helping The People That Needs It The Most

 
There are marginalized groups in history that have been the victims of technological development. These are women, youth, children, and minorities.   Even if the aim is to develop a deepfake detector, there are lots of social problems associated. Would developers in other countries be willing to make it available? Will it be qualified to detect electoral fakes, gender issues sexual harassment? These issues will continue to resurface even if deepfake detectors are now being created.       
 

Ways To Detect a Deepfake Video

 
What is incredible is that, for now, high-powered technology is not needed to detect a deepfake video. With the aid of human content moderators, deepfake videos are being determined. It just includes specific essential hacks that are required.  
 
Here are some quick and easy hacks that will help you to decide whether the video circulated or the suspect video message captured is false or not.
 

1. Poor Audio

 
Often deepfake video makers concentrate on how the video will be made, falling short of the audio moving ahead. Typically, as this happens, the outcome gives way to determine quickly whether the video is a deepfake. The video clip can contain low-quality lip-synching and robotic-sounding voices. Strange word expression that does not synchronize with how the characters open their mouths can also be observed.
 

2. Video Quality On The Big Screen

 
Fabricated recordings are not readily recognizable when viewed on a tiny screen. Particularly if viewed on smartphones or other handheld devices. However, it would be easier to notice all the edits performed on the deepfake footage when footage is viewed on the full screen on your laptop or TV. It will help check the elements made to make it look like a legitimate video. It can also display other conflicting evidence that appears on the computer. Slow down the replay time, so you can use video editing techniques to analyze elements deeper. 
 

3. Body Posture

 
Check if a person has an awkward body pose in the video. It can help you quickly identify a deepfake clip.  A deepfake video can show a conflicting position between the head and the body’s orientation. Whereas an average person prefers to generate movement in videos.
 
Most deepfake videos concentrate on facial features. That is why the best way to know whether the video is real is to check for unpleasant body posture. Many developers may not waste time synchronizing the head and the body’s movement, usually throwing them out.
 

4. Lack Of Emotion

 
By reviewing the feelings displayed by the character in the film, you can easily detect a fake video. If someone does not show or express the feelings desired in the frame’s atmosphere, verify if it has distorted footage. Check also also if you can identify stiches throughout the video.
 

5. Normal Gaze

 
An average human blinks 15-20 times each minute. It is also a facial expression, either off or on cam, that an average person makes. It is challenging to develop realistic eyes for deepfake, making it one of the most significant bumps for deepfake makers.
 
Some deepfake videos have sleepy eyes or an unusual, very peculiar look. If a particular person does not wink or does not twitch within an anticipated timeline, it may be a perfect sign to conclude that the video is fake.

6. Frizz and Flyaways

 
Unmanaged hair is another of the significant challenges for deepfake developers. An ordinary person does the minute things that make a person’s hair fall to the forehead or stand out.  A person’s hair may come astray in a traditional video while an individual is interviewed, making a speech, or just recording himself
 
You will not see any frizz or flyaway in a deepfake video because there is no actual human inside the video clip. In deepfake videos, hairs usually are not noticeable, making it a takeaway if you want to assess whether a particular video is falsified or not.
 

Final Thoughts

 
The fight for deepfake video identification is now rising. As makers of these videos get smarter and quickly overrun some deepfake identification software, it has become a difficult feat.
 
Try to do all these hacks on any malicious video you can find. This move could ensure that a deepfake fiasco will not fool you. If it doesn’t work out, but since you’re still suspicious of a particular video that you’ve seen or got somewhere on the website, make a call to the other party. Place a video call and check for yourself if it’s a real thing.

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