September 2026 Summaries

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Deepfake detection heatmaps visualize the image or video regions that most influenced a model’s synthetic-media verdict, providing spatial context that confidence scores alone cannot offer. They commonly highlight areas prone to generative inconsistencies, including eyes, mouth interiors, jawlines, hairlines, skin textures, and face-background blending boundaries, but they are attribution tools rather than conclusive proof of manipulation because compression, lighting, and source quality can also affect results. Different methods, including Grad-CAM, pixel-level saliency maps, transformer attention maps, and image forgery localization techniques, vary in granularity and interpretability, making documentation of the method important for audits and legal review. The material argues that heatmaps are most valuable when combined with verdicts, confidence and localization scores, metadata, reviewer notes, and chain-of-custody information in human-reviewed evidence packages that can reduce false-positive escalations. It describes Resemble AI’s Detect product as returning heatmaps and Image Forgery Localization scores for image and frame-level video analysis, while its Intelligence layer provides forensic explanations, fraud classifications, liveness information, and speaker profiling, with deployment and retention options intended for enterprise compliance requirements.