Detecting AI-Generated Images via Fine-Tuned SigLIP 2: A Sigmoid-Contrastive Vision-Language Encoder for Binary Authenticity Classification

The accelerating sophistication of generative artificial intelligence has produced widespread availability of photorealistic synthetic imagery, creating serious challenges for digital media integrity, forensic evidence reliability, and public trust in visual information. In response, developing robust yet computationally feasible automated detection systems has become a research priority. This study examines whether fine-tuning Sigmoid Loss for Language-Image Pre-training SigLIP2- a multilingual vision-language encoder- enables reliable discrimination between human-captured photographs and algorithmically generated images. The SigLIP2 base variant (google/siglip2-base-patch16-224) was adapted by replacing its contrastive projection head with a single linear classification layer applied to the pre-trained vision encoder. Evaluation on a held-out, balanced test set of 20,000 images yielded a classification accuracy of 98.90%, with macro-averaged and weighted F1-scores of 0.9890 each. These results demonstrate that the representational capabilities acquired by SigLIP2 through its staged pretraining- including sigmoid-based contrastive alignment, decoder-enforced spatial localization, and late-stage self-distillation- transfer effectively to the forensic discrimination of authentic versus synthetic imagery. The proposed framework is fully reproducible, and its potential applications include automated content moderation, digital forensics for legal contexts, investigative journalism verification workflows, and social media integrity systems.