Unravelling AI-Generated Misinformation in the 'Delhi Chalo' Farmers' Protest

Introduction

Social media has emerged as a leading source of communication and information; its relevance cannot be ignored during natural disasters since it is relied upon by governments and disaster relief organisations as a tool for disseminating aid and relief-related resources and communications instantly. During disaster times, social media has emerged as a primary source for affected populations to access information on relief resources; community forums offering aid resources and official government channels for government aid have enabled efficient and timely administration of relief initiatives.

However, given the nature of social media, misinformation risks during natural disasters has also emerged as a primary concern that severely hampers aid administration during natural disasters. The disaster-disinformation network offers some sensationalised influential campaigns against communities at their most vulnerable. Victims who seek reliable resources during natural calamities often reach out to inhospitable campaigns and may experience delayed or lack of access to necessary healthcare, significantly impacting their recovery and survival. This delay can lead to worsening medical conditions and an increased death toll among those affected by the disaster. Victims may lack clear information on the appropriate agencies to seek assistance from, causing confusion and delays in receiving help. 

Misinformation Threat Landscape during Natural Disaster

During the 2018 floods in Kerala, it was noted that a fake video on water leakage from the Mullaperyar Dam created panic among the citizens and negatively impacted the rescue operations. Similarly, in 2017, reports emerged claiming that Hurricane Irma had caused sharks to be displaced onto a Florida highway. Similar stories, accompanied by the same image, resurfaced following Hurricanes Harvey and Florence. The disaster-affected nation may face international criticism and fail to receive necessary support due to its perceived inability to manage the crisis effectively. This lack of confidence from the global community can further exacerbate the challenges faced by the nation, leaving it more vulnerable and isolated in its time of need.

The spread of misinformation through social media severely hinders the administration of aid and relief operations during natural disasters since it hinders first responders' efforts to counteract and reduce the spread of misinformation, rumours, and false information and declines public trust in government, media, and non-governmental organisations (NGOs), who are often the first point of contact for both victims and officials due to their familiarity with the region and the community. In Moldova, it was noted that foreign influence has exploited the ongoing drought to create divisions between the semi-autonomous regions of Transnistria and Gagauzia and the central government in Chisinau. News coverage critical of the government leverages economic and energy insecurities to incite civil unrest in this already unstable region. Additionally, First responders may struggle to locate victims and assist them to safety, complicating rescue operations. The inability to efficiently find and evacuate those in need can result in prolonged exposure to dangerous conditions and a higher risk of injury or death.

Further, international aid from other countries could be impeded, affecting the overall relief effort. Without timely and coordinated support from the global community, the disaster response may be insufficient, leaving many needs unmet. Further, misinformation also impedes military, reducing the effectiveness of rescue and relief operations. Military assistance often plays a crucial role in disaster response, and any delays can hinder efforts to provide immediate and large-scale aid.

Misinformation also creates problems of allocation of relief resources to unaffected areas which resultantly impacts aid processes for regions in actual need. Following the April 2015 earthquake in Nepal, a Facebook post claimed that 300 houses in Dhading needed aid. Shared over 1,000 times, it reached around 350,000 people within 48 hours. The originator aimed to seek help for Ward #4’s villagers via social media. Given the average Facebook user has 350 contacts, the message was widely viewed. However, the need had already been reported on quakemap.org, a crisis-mapping database managed by Kathmandu Living Labs, a week earlier. Helping Hands, a humanitarian group was notified on May 7, and by May 11, Ward #4 received essential food and shelter. The re-sharing and sensationalisation of outdated information could have wasted relief efforts since critical resources would have been redirected to a region that had already been secured.

Policy Recommendations

Perhaps the most important step in combating misinformation during natural disasters is the increasing public education and the rapid, widespread dissemination of early warnings. This was best witnessed in the November 1970 tropical cyclone in southeastern Bangladesh, combined with a high tide, struck southeastern Bangladesh, leaving more than 300,000 people dead and 1.3 million homeless. In May 1985, when a comparable cyclone and storm surge hit the same area, local dissemination of disaster warnings was much improved and the people were better prepared to respond to them. The loss of life, while still high (at about 10,000), the numbers were about 3% of that in 1970. On a similar note, when a devastating cyclone struck the same area of Bangladesh in May 1994, fewer than 1,000 people died. In India, the 1977 cyclone in Andra Pradesh killed 10,000 people, but a similar storm in the same area 13 years later killed only 910. The dramatic difference in mortalities was owed to a new early-warning system connected with radio stations to alert people in low-lying areas. 

Additionally, location-based filtering for monitoring social media during disasters is considered as another best practice to curb misinformation. However, agencies should be aware that this method may miss local information from devices without geolocation enabled. A 2012 Georgia Tech study found that less than 1.4 percent of Twitter content is geolocated. Additionally, a study by Humanity Road and Arizona State University on Hurricane Sandy data indicated a significant decline in geolocation data during weather events.

Alternatively, Publish frequent updates to promote transparency and control the message. In emergency management and disaster recovery, digital volunteers—trusted agents who provide online support—can assist overwhelmed on-site personnel by managing the vast volume of social media data. Trained digital volunteers help direct affected individuals to critical resources and disseminate reliable information. 

Enhancing the quality of communication requires double-verifying information to eliminate ambiguity and reduce the impact of misinformation, rumors, and false information must also be emphasised. This approach helps prevent alert fatigue and "cry wolf" scenarios by ensuring that only accurate, relevant information is disseminated. Prioritizing ground truth over assumptions and swiftly releasing verified information or acknowledging the situation can bolster an agency's credibility. This credibility allows the agency to collaborate effectively with truth amplifiers. Prebunking and Debunking methods are also effective way to counter misinformation and build cognitive defenses to recognise red flags. Additionally, evaluating the relevance of various social media information is crucial for maintaining clear and effective communication.

References

1. https://www.nature.com/articles/s41598-023-40399-9#:~:text=Moreover%2C%20misinformation%20can%20create%20unnecessary,impacting%20the%20rescue%20operations29. 
2. https://www.redcross.ca/blog/2023/5/why-misinformation-is-dangerous-especially-during-disasters
3. https://www.soas.ac.uk/about/blog/disinformation-during-natural-disasters-emerging-vulnerability‍
4. https://www.dhs.gov/sites/default/files/publications/SMWG_Countering-False-Info-Social-M dia-Disasters-Emergencies_Mar2018-508.pdf

Executive Summary:

One of the most complex threats that have appeared in the space of network security is focused on the packet rate attacks that tend to challenge traditional approaches to DDoS threats’ involvement. In this year, the British based biggest Internet cloud provider of Europe, OVHcloud was attacked by a record and unprecedented DDoS attack reaching the rate of 840 million packets per second. Targets over 1 Tbps have been observed more regularly starting from 2023, and becoming nearly a daily occurrence in 2024. The maximum attack on May 25, 2024, got to 2.5 Tbps, this points to a direction to even larger and more complex attacks of up to 5 Tbps. Many of these attacks target critical equipment such as Mikrotik models within the core network environment; detection and subsequent containment of these threats prove a test for cloud security measures.

Modus Operandi of a Packet Rate Attack:

A type of cyberattack where an attacker sends with a large volume of packets in a short period of time aimed at a network device is known as packet rate attack, or packet flood attack or network flood attack under volumetric DDoS attack. As opposed to the deliberately narrow bandwidth attacks, these raids target the computation time linked with package processing. 

Key technical characteristics include: 

• Packet Size: Usually compact, and in many cases is less than 100 bytes 
• Protocol: Named UDP, although it can also involve TCP SYN or other protocol flood attacks 
•  Rate: Exceeding 100 million packets per second (Mpps), with recent attacks exceeding 840 Mpps 
• Source IP Diversity: Usually originating from a small number of sources and with a large number of requests per IP, which testifies about the usage of amplification principles 
• Attack on the Network Stack : To understand the impact, let's examine how these attacks affect different layers of the network stack:

1. Layer 3 (Network Layer):

• Each packet requires routing table lookups and hence routers and L3 switches have the problem of high CPU usage. 
• These mechanisms can often be saturated so that network communication will be negatively impacted by the attacker.

2. Layer 4 (Transport Layer):

• Other stateful devices (e.g. firewalls, load balancers) have problems with tables of connections 
• TCP SYN floods can also utilize all connection slots so that no incoming genuine connection can be made.

3. Layer 7 (Application Layer): 

• Web servers and application firewalls may be triggered to deliver a better response in a large number of requests 
• Session management systems can become saturated, and hence, the performance of future iterations will be a little lower than expected in terms of their perceived quality by the end-user. 

Technical Analysis of Attack Vectors

Recent studies have identified several key vectors exploited in high-volume packet rate attacks:

1.MikroTik RouterOS Exploitation:

• Vulnerability: CVE-2023-4967
• Impact: Allows remote attackers to generate massive packet floods
• Technical detail: Exploits a flaw in the FastTrack implementation

2.DNS Amplification:

• Amplification factor: Up to 54x
• Technique: Exploits open DNS resolvers to generate large responses to small queries
• Challenge: Difficult to distinguish from legitimate DNS traffic

3.NTP Reflection:

• Command: monlist
• Amplification factor: Up to 556.9x
• Mitigation: Requires NTP server updates and network-level filtering

Mitigation Strategies: A Technical Perspective

1. Combating packet rate attacks requires a multi-layered approach:

• Hardware-based Mitigation:
• Implementation: FPGA-based packet processing
• Advantage: Can handle millions of packets per second with minimal latency
• Challenge: High cost and specialized programming requirements

2.Anycast Network Distribution:

• Technique: Distributing traffic across multiple global nodes
• Benefit: Dilutes attack traffic, preventing single-point failures
• Consideration: Requires careful BGP routing configuration

3.Stateless Packet Filtering:

• Method: Applying filtering rules without maintaining connection state
• Advantage: Lower computational overhead compared to stateful inspection
• Trade-off: Less granular control over traffic

4.Machine Learning-based Detection:

• Approach: Using ML models to identify attack patterns in real-time
• Key metrics: Packet size distribution, inter-arrival times, protocol anomalies
• Challenge: Requires continuous model training to adapt to new attack patterns

Performance Metrics and Benchmarking

When evaluating DDoS mitigation solutions for packet rate attacks, consider these key performance indicators:

• Flows per second (fps) or packet per second (pps) capability
• Dispersion and the latency that comes with it is inherent to mitigation systems.
• The false positive rate in the case of the attack detection
• Exposure time before beginning of mitigation from the moment of attack

Way Forward

The packet rate attacks are constantly evolving where the credible defenses have not stayed the same. The next step entails extension to edge computing and 5G networks for distributing mitigation closer to the attack origins. Further, AI-based proactive tools of analysis for prediction of such threats will help to strengthen the protection of critical infrastructure against them in advance.

In order to stay one step ahead in this, it is necessary to constantly conduct research, advance new technologies, and work together with other cybersecurity professionals. There is always a need to develop secure defenses that safeguard these networks.

Reference:

https://blog.ovhcloud.com/the-rise-of-packet-rate-attacks-when-core-routers-turn-evil/

https://cybersecuritynews.com/record-breaking-ddos-attack-840-mpps/

https://www.cloudflare.com/learning/ddos/famous-ddos-attacks/

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Introduction

In the dynamic intersection of pop culture and technology, an unexpected drama unfolded in the virtual world, where the iconic Taylor Swift account has been temporarily blocked on X . The incident sent a shockwave through the online community, sparking debates and speculation about the misuse of deepfake technology.

Taylor Swift's searches on social media platform X have been restored after a temporary blockage was lifted following outrage over her explicit AI images. The social media site, formerly known as Twitter, temporarily restricted searches for Taylor Swift as a temporary measure to address a flood of AI-generated deepfake images that went viral across X and other platforms. 

X has mentioned it is actively removing the images and taking appropriate actions against the accounts responsible for spreading them. While Swift has not spoken publicly about the fake images, a report stated that her team is "considering legal action" against the site which published the AI-generated images.

The Social Media Frenzy

As news of temporary blockages spread like wildfire across social media platforms, users engaged in a frenzy of reactions. The fake picture was re-shared 24,000 times, with tens of thousands of users liking the post. This engagement supercharged the deepfake image of Taylor Swift, and by the time the moderators woke up, it was too late. Hundreds of accounts began reposting it, which started an online trend. Taylor Swift's AI video reached an even larger audience. The source of the photograph wasn't even known to begin with. The revelations are causing outrage. American lawmakers from across party lines have spoken. One of them said they were astounded, while another said they were shocked. 

AI Deepfake Controversy

The deepfake controversy is not new. There are lot of cases such as Rashmika Mandana, Sachin Tendulkar, and now Taylor Swift have been the victims of such misuse of Deepfake technology. The world is facing a concern about the misuse of AI or deepfake technology. With no proactive measures in place, this threat will only worsen affecting privacy concerns for individuals. This incident has opened a debate among users and industry experts on the ethical use of AI in the digital age and its privacy concerns.

Why has the Incident raised privacy concerns?

The emergence of Taylor Swift's deepfake has raised privacy concerns for several reasons.

1. Misuse of Personal Imagery:  Deepfake uses AI and its algorithms to superimpose one person’s face onto another person’s body, the algorithms are processed again and again till the desired results are obtained. In the case of celebrities or higher-position people, it's very easy for crooks to get images and generate a deepfake.  In the case of Taylor Swift, her images are misused. The misuse of Images can have serious consequences for an individual's reputation and privacy.

2. False narrative and Manipulation:  Deepfake opens the door for public reaction and spreads false narratives, causing harm to reputation, and affecting personal and professional life. Such false narratives through deepfakes may influence public opinion and damage reputation making it challenging for the person to control it. 

3. Invasion of Privacy: Creating a deepfake involves gathering a significant amount of information about their targets without their consent. The use of such personal information for the creation of AI-generated content without permission raises serious privacy concerns. 

4. Difficulty in differentiation: Advanced Deepfake technology makes it difficult for people to differentiate between genuine and manipulated content. 

5. Potential for Exploitation: Deepfake could be exploited for financial gain or malicious motives of the cyber crooks. These videos do harm the reputation, damage the brand name, and partnerships, and even hamper the integrity of the digital platform upon which the content is posted, they also raise questions about the platform’s policy or should we say against the zero-tolerance policy on posting the non-consensual nude images.

Is there any law that could safeguard Internet users?

Legislation concerning deepfakes differs by nation and often spans from demanding disclosure of deepfakes to forbidding harmful or destructive material. Speaking about various countries, the USA including its 10 states like California, Texas, and Illinois have passed criminal legislation prohibiting deepfake. Lawmakers are advocating for comparable federal statutes. A Democrat from New York has presented legislation requiring producers to digitally watermark deepfake content. The United States does not criminalise such deepfakes but does have state and federal laws addressing privacy, fraud, and harassment.

In 2019, China enacted legislation requiring the disclosure of deepfake usage in films and media. Sharing deepfake pornography became outlawed in the United Kingdom in 2023 as part of the Online Safety Act.

To avoid abuse, South Korea implemented legislation in 2020 criminalising the dissemination of deepfakes that endanger the public interest, carrying penalties of up to five years in jail or fines of up to 50 million won ($43,000).

In 2023, the Indian government issued an advisory to social media & internet companies to protect against deepfakes that violate India'sinformation technology laws. India is on its way to coming up with dedicated legislation to deal with this subject.

Looking at the present situation and considering the bigger picture, the world urgently needs strong legislation to combat the misuse of deepfake technology.

Lesson learned

The recent blockage of Taylor Swift's searches on Elon Musk's X has sparked debates on responsible technology use, privacy protection, and the symbiotic relationship between celebrities and the digital era. The incident highlights the importance of constant attention, ethical concerns, and the potential dangers of AI in the digital landscape. Despite challenges, the digital world offers opportunities for growth and learning.

Conclusion

Such deepfake incidents highlight privacy concerns and necessitate a combination of technological solutions, legal frameworks, and public awareness to safeguard privacy and dignity in the digital world as technology becomes more complex.

References:

• https://www.hindustantimes.com/world-news/us-news/taylor-swift-searches-restored-on-elon-musks-x-after-brief-blockage-over-ai-deepfakes-101706630104607.html
• https://readwrite.com/x-blocks-taylor-swift-searches-as-explicit-deepfakes-of-singer-go-viral/

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