Synthetic media detection encompasses techniques for recognizing all types of AI-generated content — text from large language models, images from diffusion models or GANs, synthetic audio from text-to-speech systems, and AI-generated video. It is broader than deepfake detection, covering any content produced by generative AI.

Detection by Modality

• Text Detection: Analyze statistical patterns that differ between human and machine writing — perplexity (how "surprised" a language model is by text), burstiness (variation in sentence complexity), and token probability distributions. Human writing tends to be more variable and surprising than LLM output.
• Image Detection: Examine pixel-level artifacts, frequency domain signatures, and metadata inconsistencies. GAN-generated images show characteristic spectral peaks, and diffusion model outputs have specific noise patterns.
• Audio Detection: Analyze spectrograms for synthesis artifacts — unnatural formant transitions, missing breath sounds, and overly consistent pitch.
• Video Detection: Combine frame-level image analysis with temporal consistency checks — synthetic video often shows subtle inter-frame artifacts.

Detection Tools and Systems

• GPTZero: Detects AI-generated text by analyzing perplexity and burstiness patterns.
• Originality.AI: Commercial AI text detection service for content creators and publishers.
• GLTR (Giant Language Model Test Room): Visualizes token-level probabilities to highlight likely AI-generated passages.
• SynthID (Google): Watermark-based detection for AI-generated images and text.
• Hive Moderation: Multi-modal AI content detection API.

Challenges

• Quality Convergence: As generative models improve, the statistical differences between human and synthetic content shrink.
• Model Diversity: Each generation model leaves different artifacts — detectors trained on one model may miss another.
• Post-Processing Destruction: Compression, resizing, cropping, re-encoding, and screenshotting can destroy forensic evidence.
• Adversarial Evasion: Producers can use paraphrasing, noise addition, or adversarial perturbations to evade detection.
• False Positives: Non-native English speakers and formulaic writing styles can be falsely flagged as AI-generated.

Detection Strategies

• Watermarking: Embed imperceptible marks during generation for later identification (proactive approach).
• Forensic Analysis: Analyze content for generation artifacts after the fact (reactive approach).
• Provenance Tracking: Use C2PA standards to record content origin and modification history.
• Ensemble Methods: Combine multiple detection signals for more robust classification.

Synthetic media detection is a rapidly evolving field where no single method provides perfect accuracy — effective detection combines multiple approaches and maintains regularly updated models.