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AI-generated image detection on ConImageGen

99.6Average AccuracyDTS-Det
47.70461.17774.6588.123Jul 4, 2026

Evaluation results

MethodLinks
DTS-DetTexture-relation stream=DINOv3, Semantic cue stream=SigLIP22026.0799.610010010010010010099.699.610010010010010010010010099.299.299.599.596.896.910010099.599.599.5
DTS-DetType=Ours2026.0794.2——————————————————————————93.3
SigLIP2+DINOv32026.0789.196.597.29897.992.792.790.490.597.197.197.497.498.498.498.298.263.449.288.686.365.553.491.391.381.178.785.4
AIDE2026.0787.397.697.685.486.47676.489.989.897.797.797.697.69493.997.897.982.780.277.477.250.443.597.297.391.190.986.6
SigLIP2+DINOv3Type=Generic Classifier2026.0784.5——————————————————————————82.4
DINOv32026.0784.281.577.697.797.892.291.786.885.297.997.997.997.996.996.898.498.460.747.578.680.352.941.18889.165.564.382
NPR2026.0779.793.393.578.879.581.48284.283.581.782.383.383.283.583.383.984.159.657.888.48864.362.187.587.665.665.479.4
F3Net2026.0778.983.783.385.986.383.68490.290.192.993.190.790.97069.891.491.25552.878.278.452.143.787.586.66460.678.2
UnivFD2026.0778.777.471.894.694.594.294.176.671.788.287.190.289.787.786.383.981.756.739.670.371.25646.288.189.958.951.174.9
FreqNet2026.0776.993.393.576.678.67677.582.382.689.689.789.889.377.578.685.68564.663.272.271.255.75572.971.463.162.276.8
NPRType=Semantic-irrelevant2026.0775.3——————————————————————————76.1
AIDEType=Semantic2026.0774.8——————————————————————————73.9
Xception2026.0770.766.366.973.172.673.874.280.280.586.285.784.784.865.664.981.280.958.957.863.262.756.35568.667.867.865.770.7
C2P-CLIP2026.0770.471.66591.191.490.490.874.87174.468.97672.876.775.474.469.453.761.957.76552.456.465.273.156.766.871.4
UnivFDType=Semantic2026.0770.4——————————————————————————71.3
DINOv3-LoRAType=Generic Classifier2026.0770.3——————————————————————————60.1
SigLIP2-LoRAType=Generic Classifier2026.0768——————————————————————————64.5
ResNet502026.0767.968.869.654.154.668.669.97877.779.279.682.78362.86277.778.3565764.363.553.151.472.572.564.66367.8
SigLIP22026.0767.66155.254.24273.572.885.486.985.688.986.688.770.168.185.487.250.159.857.769.149.265.165.47455.264.970.8
F3NetType=Frequency2026.0767.6——————————————————————————68.1
XceptionType=Generic Classifier2026.0766.3——————————————————————————66.4
C2P-CLIPType=Semantic2026.0764.1——————————————————————————63.8
ResNet50Type=Generic Classifier2026.0763.7——————————————————————————62.4
FreqNetType=Frequency2026.0763.2——————————————————————————61.3
Noiseprint++Type=Semantic-irrelevant2026.0750.2——————————————————————————67.9
Noiseprint++2026.0749.748.963.954.669.453.768.453.468.648.363.64762.448.96448.963.852.767.451.267.150.966.939.353.848.162.164.7