Deepfake Content Analysis Using Error Level Analysis and Metadata

Abstract

Purpose: The spread of deepfakes on platform X threatens the integrity of digital information, but previous research has applied Metadata Analysis, Error Level Analysis (ELA), and Reality Defender separately, not yet as an integrated approach. This study applied Metadata Analysis and ELA to identify manipulation in deepfake images, as well as evaluate the effectiveness of Reality Defender in detecting deepfake content in images circulating on platform X as an application case study. Methods/Study design/approach: Using a descriptive qualitative approach with digital forensic experiment methods, seven purposive selected image samples representing seven content characteristic scenarios (original, face-swap, GAN, full generative AI, anti-forensics, conventionally edited, and platform compressed), analyzed through three layered stages with tiered final classification criteria based on the cross-validated Reality Defender score threshold with ELA. Result/Findings: Metadata is only informative before uploading, as X deletes EXIF uniformly post-upload. ELA remained effective in both conditions, showing localized intensity anomalies (close to 240-255 from 255) across deepfake samples. Reality Defender correctly classified six of the seven samples (85.7% accuracy, 100% in the deepfake category, 66.7% in the original category), with one false positive (64%) in the original, conventionally edited image. Novelty/Originality/Value: This study integrated all three methods simultaneously and layered with explicit criteria, showing Metadata lost post-upload diagnostic value while ELA and Reality Defender remained the most reliable for content sourced from platform X.