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During the ongoing civil conflict in Sudan, AI-generated deepfakes and synthetic audio have been widely deployed as tools for disinformation and psychological warfare. These tools have been used to impersonate political and military leaders, incite potential violence, and manipulate public opinion. The situation is further complicated by a 'liar's dividend,' where authentic evidence is dismissed as AI-generated, and a lack of effective detection tools or local-context-aware moderation on social media platforms.
Purported AI-generated deepfakes have circulated widely on Sudanese social media during the ongoing conflict, allegedly spreading false claims, impersonating public figures, and distorting political discourse. Reported incidents include fabricated videos and audio used to mislead or satirize, some of which gained traction among journalists and officials. Observers warn that poor detection tools and limited moderation may be enabling the manipulation of public trust.
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 and the resulting 'liar's dividend' have severely degraded the information ecosystem in Sudan, undermining shared reality.
The incident involves coordinated campaigns using AI-generated deepfakes to spread disinformation and influence public opinion at scale during a national conflict.
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 deepfakes were intentionally created and spread by human actors to conduct psychological warfare and manipulate public opinion after the AI models were deployed.
Added categories peak at 4: Severe (Epistemic Harm, Indirect)
During the Sudanese civil war, AI-generated deepfakes and synthetic audio have been systematically deployed for psychological warfare and disinformation. This includes fake coup recordings broadcast on National TV and fabricated military orders. The resulting 'liar's dividend' has severely degraded the information ecosystem, posing substantial threats to sovereignty and societal stability.
The AI systems (deepfake generators, voice cloning models) are used by human actors as tools to perform specific, well-defined functions (generating synthetic media) based on direct inputs without autonomous planning or agency.
Limited Risk: The report describes AI-generated content such as deepfakes, which require specific transparency obligations to ensure users are informed.
The AI systems were primarily used to generate deepfake videos and clone voices of political and military figures to spread disinformation.
The report provides multiple concrete examples of deepfakes used in Sudan, citing specific platforms (TikTok, Facebook, X), view counts (230k), and dates. It also includes perspectives from fact-checkers (Beam Reports) and AI researchers.
Some content was claimed to be satirical, but later morphed into disinformation, showing ambiguity in intent for some creators.
Technology
Inaccurate Detection Tools
Public detection tools produce frequent false positives and lack clarity.
Impact of File Compression
Compression and low resolution reduce deepfake detection accuracy.
Weak Low-Resource Voice Models
Voice cloning models are less effective in low-resource dialects.
Data Inputs
Lack of Sudanese Dialect Data
AI models lack training data on specific Sudanese dialects.
Biased Training Datasets
Detection tools depend heavily on specific training data quality.
Human Factors
Sharing of Unverified Content
Politicians and public shared fake media without verification.
Exploitation of Liars Dividend
Politicians falsely claim authentic recordings are AI-generated.
Lack of Technical Expertise
General public struggles to interpret complex detection results.
Process and Methods
Lack of Reliable Detection Tools
Fact-checkers lack access to easy and reliable verification tools.
Absence of On-Ground Reporting
Conflict prevents direct reporting, increasing disinformation.
Poor Platform Policy Enforcement
Social media policies are not effectively enforced in Sudan.
Management
Inadequate Content Moderation
Platforms lack moderators who understand the local Sudanese context.
AI system involved
An AI system was involved, but the reports do not identify it.
Identified by automated extraction; can differ from the alleged developers above.
