Compression‐Resilient, Resolution‐Vulnerable: A Statistical Robustness Analysis of EfficientNet‐B2 for AI‐Generated Image Detection

Reliable classification of AI-generated images requires more than high accuracy on unmodified test examples: media files may be compressed or downsampled before forensic analysis. This paper presents a controlled, paired robustness evaluation of a previously developed EfficientNet-B2 detector using RGB, fast Fourier transform (FFT), and artifact-oriented image features. The existing checkpoint, validation-selected classification threshold, and fixed set of 6,000 held-out images were reused without training or test-time threshold adjustment. Six transformed conditions were evaluated against original inputs: JPEG qualities 90, 70, and 50, and bicubic downsampling to maximum dimensions of 256, 128, and 64 pixels followed by reconstruction to the original dimensions. The baseline achieved 94.53% accuracy and 0.9857 area under the receiver operating characteristic curve (AUROC). JPEG variants produced accuracies between 94.38% and 94.63%, with paired 95% bootstrap intervals of accuracy change spanning zero. By contrast, downsampling to 256, 128, and 64 pixels yielded accuracies of 93.15%, 91.73%, and 74.40%, respectively. At 64 pixels, the accuracy difference was −20.13 percentage points (95% paired bootstrap interval approximately −21.12 to −19.12), and AUROC fell to 0.8967. The evidence indicates substantially greater sensitivity to severe spatial resolution loss than to the tested JPEG compression settings. Because the existing training procedure incorporates JPEG augmentation, these findings characterize this detector and evaluation protocol rather than establish universal robustness across detectors or unseen transformations.