Addressing the Implications of Deepfakes in Election Seasons

As 2024 matures, we all witness an astonishing year for democracy and technology. Almost half the population will elect their leaders this year, the highest of any year in human history. Coincidentally, this is also the year where AI has seen the biggest development since its inception.

While AI offers extraordinary benefits, most of which are yet to be discovered, it has also enabled fraudsters to mislead voters by creating deepfakes of candidates and other people involved with the election.

The whole concept of electing a leader involves people having the power; however, deep fakes are, directly and indirectly, shaping people’s decisions. And the rise of deepfakes is casting a shadow on the people’s power. This blog sheds light on the dark intersection of AI and elections, exploring how deepfakes could influence the democratic process and what we can do to preserve the sanctity of our votes.

Understanding Deepfakes

Deepfakes are synthetic media in which a person’s likeness is replaced with someone else’s, making it appear as though they are saying or doing things they never actually did. This technology has become infamous for its potential to create convincing fake videos and audio recordings.

The term “deepfake” is a blend of “deep learning” and “fake,” reflecting the deep learning algorithms that generate these falsified results.

The Technology Behind Deepfakes

At the core of deepfake technology are Generative Adversarial Networks (GANs), a class of machine learning frameworks.

GANs have two parts: a generator that makes images or videos, and a discriminator that assesses their authenticity. The generator produces increasingly convincing fakes as it learns from the discriminator’s feedback, leading to a rapid improvement in the quality of generated media.

The Process of Creating Deepfakes

Creating a deepfake involves several steps: first, a large dataset of images or videos of the target person is compiled. Then, the GAN is trained on this data until it can produce a convincing likeness of the person. The final step is refining the output to ensure that the audio syncs with the video and that the result is as realistic as possible.

Detecting Deepfakes

Detecting deepfakes is challenging because the technology is constantly evolving. However, experts use a combination of methods, including looking for inconsistencies in lighting, shadows, and facial expressions (more on this later). AI-based detectors analyze videos for signs that are imperceptible to the human eye, such as irregular blinking patterns or subtle distortions in the image.

The Implications of Deepfakes During Elections

Elections are a cornerstone of democratic societies, but the rise of deepfake technology poses new challenges. Deepfakes can be used in elections to create convincing forgeries of candidates saying or doing things that never happened, potentially swaying public opinion and undermining democratic integrity.

Misinformation and Public Perception

Deepfakes can spread misinformation at an alarming rate, as all it takes is a few seconds to share a viral clip on social media. So, by fabricating events or statements, they can significantly alter what the voters think about candidates, leading to misinformed decision-making.

Targeting Political Figures

Political figures are prime targets for deepfake attacks. These can be used to discredit or embarrass politicians, making it difficult to maintain a fair political discourse and challenging the very foundation of trust in the electoral process.

Challenges for Election Security

One of the greatest challenges for election security is ensuring the authenticity of political content. Deepfakes interfere with this, which is why we are in dire need of advanced strategies and technologies to protect elections from such interference.

How to Spot Deep Fakes During Election

With deep fake technology improving, it is getting more difficult to distinguish the real from the fake, especially for people who need to be made aware of this technology. However, people with a keen eye can find deepfakes, if told what and where to look. Here is how anyone can manually detect deepfakes:

Facial Inconsistencies

Mismatched Lip Syncing: Look for inconsistencies between lip movements and spoken words. Deepfake technology may not perfectly align audio with visual cues, yet.
Facial Expressions: Check for any unnatural expressions or movements that seem out of place or don’t match the context of the conversation. Unless it’s the most advanced deep fake, the expressions may not look human.

Analyzing Eyes and Blinking

Blinking Patterns: Humans blink at a regular rate, typically every 2-10 seconds. A lack of blinking or excessive blinking in a video can signal manipulation.
Eye Movement: The eyes should naturally move in coordination with speech and expression. It is simply human. Unusual eye behaviour can be a sign of a deepfake.

Lighting and Shadows

Consistency with Environment: Check if the lighting on the subject’s face matches the surrounding environment. Inconsistencies can reveal editing. This is how movie experts find out if a scene is real or CGI.
Shadow Direction: Shadows should align with the light source. It is basic physics and deep fake creators may not have the time to put realistic shadows. If the shadows don’t match, it could suggest the image has been altered.

Audio Verification

Voice Authenticity: Compare the voice in the video to previous, real recordings of the individual. Listen for differences in pitch, tone, and cadence.
Background Noise: Listen for any signs of splicing or editing, such as sudden changes in background noise or ups and downs in audio quality.

Contextual Clues

Source Credibility: Consider the origin of the video. Authentic news videos are likely to come from only reputable sources.
Content Plausibility: Reflect on whether the content of the video is consistent with the known beliefs and character of the individual.
Low Resolution: These days, even cheap smartphones shoot high-quality videos. However, most deep fake videos during election time are very low-quality (because it prevents us from checking for details). This can be a first indication of whether you are watching a real speech by a political candidate or a fake one.

Cross-Referencing Content

Fact-Checking: Credible sources will post major news during election times in their social media pages. So if a video from an individual of high stature emerges from nowhere and no media is covering it on their social media, you can rest assured that it is fake.
Comparing Sources: If it is a deep fake video, chances are, you might already find a similar video of the same individual in a completely different context. The bad actor who developed the video likely used the same video to manipulate the content.

Emotional Manipulation

Intent to Harm: Be wary of videos that seem designed to provoke outrage or fear. The only goal of deepfakes during election season is to manipulate emotions and spread misinformation.

Speech and Dialogue

Speech Patterns: Pay attention to the rhythm and flow of speech. AI-generated speech may lack the natural variations of human speech.
Dialogue Content: Consider the choice of words and phrases. If you have already seen the person speaking before, does it sound like something the person would typically say?

Yes, this is a lot, but there is a lot at play in deepfakes and bad actors will use anything in their power to manipulate people during elections. While manual detection is difficult for people who are not tech-savvy, there are individuals dedicated to finding deepfakes and all it takes is getting the information before it has done its damage.

But manual verification is not robust enough for a high-value industry like the BFSI sector, where a lot of things are at stake:

How BFSI Sector is Affected by Deepfakes During Elections

1. Market Volatility Triggered by Political Deepfakes:

As mentioned earlier, election seasons often witness a surge in political deepfakes targeting key financial figures or policymakers. False statements attributed to central bankers or government officials can create market volatility, causing sudden fluctuations in stock prices and investment decisions.

2. Disinformation Campaigns Impacting Investor Confidence:

Deepfakes, when strategically released during election seasons, pose a significant threat to the stability of financial markets. They achieve this by spreading fake messages that claim to announce changes in regulations, shifts in economic policies, or the imminent threat of financial turmoil.

While the goal is to downplay a political party or candidate, the side-effect is that it instils uncertainty and fear, which could trigger widespread panic and result in mass withdrawals from banks, mutual funds, and various financial entities.

3. Heightened Cybersecurity Threats During Election Periods:

During election seasons, intense political vigour and easy access to advanced technology provide a perfect opportunity for cybercriminals to target the BFSI sector. The sector becomes vulnerable to deepfake-driven phishing attacks that focus on prominent figures or institutions. These attacks cleverly use political stories as bait, tricking people into giving away confidential financial details or accidentally taking part in fraudulent transactions.

4. Regulatory Compliance Amidst Election-Related Deepfake Risks:

Election seasons bring about significant regulatory compliance challenges for the BFSI sector. This is due to the widespread use of deepfakes, which often make it difficult to distinguish between what’s real and what’s not.

Regulators must respond quickly to such threats that manipulated media present. It’s crucial to ensure that financial institutions maintain their commitment to integrity, transparency, and consumer protection—even during the chaos of political debates.

5. Erosion of Public Trust in Financial Institutions:

Naturally, all this deepfake-driven misinformation can severely damage the public’s faith in the trustworthiness and dependability of financial institutions. False narratives spread through realistic-looking fake media reduce the credibility of official announcements from banks, insurance companies, and investment firms. This further increases doubt and skepticism among customers and investors alike.

Combating deepfakes with AI powered detection tool

As you would expect by now, the BFSI sector cannot take risks by opting for manual detection of deep fake videos and audio. Your organization requires a reliable AI-powered deep fake detection API. And when it comes to choosing the best deep fake detection tool, Arya AI has got you covered.

Arya AI’s Deepfake Detection API uses state-of-the-art algorithms and deep learning to distinguish genuine content from fake media, whether images, video, or audio. You can use this tool to uphold the integrity and reliability of your organization, protect its digital assets, and maintain public trust, especially during the sensitive times of elections.

Deepfake Detection API

Integrating Arya AI’s Deepfake Detection API into your systems strengthens your defenses against deepfake-related dangers. This API ensures the authenticity and credibility of visual content like images, videos, and audio. It is incredibly precise and efficient, whether it’s for verifying identities, spotting fraudulent activities, or fighting off disinformation campaigns.

Other Uses of Deepfake Detection API

Customer Verification Services: To prevent fraud in customer service interactions by ensuring that the person on the other end of a video call is who they claim to be.
Identity Verification: To enhance security by verifying the authenticity of customer-provided identification documents and facial recognition during video-based verification.
Fraud Prevention: To identify and mitigate convincing fake videos or audio recordings that could be used for financial fraud.
Regulatory Compliance: To demonstrate compliance with regulatory standards and safeguard sensitive customer information.
Corporate Security: To safeguard companies from deepfake attacks that could lead to corporate espionage or damage to brand reputation.
Media and Journalism: To verify the authenticity of videos and images before publishing, ensuring that news content is accurate and trustworthy.
Social Media Platforms: To detect and remove deepfake content that violates community guidelines, such as fake news or manipulated videos intended to mislead users.
Law Enforcement: To assist in criminal investigations by identifying deepfake content used in illegal activities like blackmail or fraud.
Entertainment Industry: To protect the likeness rights of celebrities and prevent unauthorized use of their image in deepfake videos.
Personal Security: For individuals to verify the authenticity of videos and images they encounter online, protecting themselves from scams and misinformation.
Legal Proceedings: To provide evidence of tampering in court cases where video or audio content is presented as evidence.
Education and Research: To develop better deepfake detection methods and educate the public about the impact of deepfakes on society.

Conclusion

We live in the age of AI, and its intersection with democracy is a double-edged sword. On one side, we promise unprecedented technological advancements; on the other, we face the specter of deepfakes threatening the foundation of our democratic processes.

That being said, individuals and industries can turn the tide against deepfakes. By fostering awareness, promoting education on deepfake detection, and harnessing advanced AI tools like Arya AI’s Deepfake Detection API, you can save yourself and your organization from cyber attacks.

The BFSI sector can particularly benefit from integrating such sophisticated detection tools. This not only safeguards the authenticity of their content but also helps uphold public trust, especially during the critical times of elections.

Don’t let deepfakes destroy your organization’s credibility. Safeguard your digital assets and maintain public trust with Arya AI’s Deepfake Detection API.