This is a research prototype. The data and analyses are preliminary and not yet validated — we'd welcome your .
During the Sudanese civil war, various actors have utilized AI-generated synthetic media, including voice cloning and deepfake videos, to impersonate political leaders and spread disinformation. These campaigns have targeted public perception and created an environment of skepticism, where even authentic recordings are sometimes falsely dismissed as AI-generated. While platforms like TikTok have removed some accounts, the proliferation of these tools continues to pose significant risks to the information ecosystem in the region.
Beginning in late August 2023, a TikTok channel called The Voice of Sudan circulated purported AI-generated audio claiming to be leaked recordings of former Sudanese president Omar al-Bashir. Experts identified several recordings as voice-cloned fabrications, derived from unrelated broadcasts by a political commentator. The campaign emerged during Sudan's civil war and appeared designed to influence political perceptions. TikTok later removed the account for violating synthetic media policies.
Domain classification, causal taxonomy, severity scores, and national security assessments were LLM-classified and may contain errors.
Using AI systems to conduct large-scale disinformation campaigns, malicious surveillance, or targeted and sophisticated automated censorship and propaganda, with the aim of manipulating political processes, public opinion, and behavior.
Also classified as
The widespread use of deepfakes has degraded the local information ecosystem, leading to a 'liar's dividend' where citizens distrust even authentic recordings.
The incident involves coordinated campaigns using AI-generated synthetic media to spread political disinformation and manipulate public opinion at scale during a civil war.
Human
Due to a decision or action made by humans
Intentional
Due to an expected outcome from pursuing a goal
Post-deployment
Occurring after the AI model has been trained and deployed
The disinformation campaigns were intentionally created and spread by human actors using post-deployment generative AI tools to manipulate public opinion during the conflict.
Added categories peak at 4: Severe (Epistemic Harm, Indirect)
During the Sudanese civil war, various actors deployed AI-generated synthetic media, including voice cloning and deepfakes, to impersonate political and military leaders. This has severely degraded the local information ecosystem, creating a 'liars dividend' where authentic information is dismissed, complicating conflict resolution and destabilizing societal trust.
The report describes the use of 'voice conversion software' and 'deepfake technologies' to mimic voices and generate fake videos. These systems operate as tools, performing specific functions on direct human input without autonomous planning or agency.
Limited Risk: The report describes 'AI-generated content such as deepfakes' and 'synthetic media', which fall under Risk Level 3 due to transparency obligations to ensure users are informed of the AI's nature.
The reports focus heavily on the use of voice conversion software to mimic political leaders, as well as the generation of deepfake videos and images shared on social media.
The reports from reputable sources (BBC, African Arguments) provide clear, consistent details about the nature of the deepfake campaigns, the platforms involved (TikTok, X, Facebook), and the specific political figures targeted. The role of AI (voice cloning and deepfake generation) is explicitly stated, and the societal impacts are well-documented.
Some might view the satirical deepfakes as harmless entertainment, but the reports show they quickly morphed into political disinformation.
Technology
Advanced Voice Conversion Tech
Voice conversion software allows easy cloning of political figures' voices.
Imperfect Detection Software
Publicly available detection tools produce frequent false positives.
Low-Resource Dialect Limitations
AI models struggle with Sudanese dialects, causing robotic tones.
Data Inputs
Lack of Localized Training Data
Low-resource languages like Sudanese dialects lack diverse training datasets.
Compression and Low Resolution
File compression and low resolution degrade detection tool accuracy.
Human Factors
Democratization of Deepfakes
Average users can quickly create and share sophisticated fake audio.
Unverified Sharing by Leaders
Politicians and public figures share unverified deepfakes on social media.
The Liar's Dividend Effect
Politicians falsely claim real recordings are AI-generated to escape blame.
Process and Methods
Absence of On-The-Ground Reporting
Lack of verified local reporting allows online disinformation to spread.
Inefficient Detection Access
Reliable deepfake detection tools are out of reach for the general public.
Poor Content Moderation Enforcement
Platforms fail to enforce deepfake policies in regions like Sudan.
Management
Insufficient Local Moderators
Platforms lack content moderators who understand the local Sudanese context.
Regulatory Environment
Lack of Platform Accountability
No strong regulatory enforcement to hold platforms liable in conflict zones.
AI system involved
Identified by automated extraction; can differ from the alleged developers above.
