Multimodal Large Language Models (MLLMs) like GPT-4V are capable of reasoning across text and image modalities, showing promise in a variety of complex vision-language tasks. In this preliminary study, we investigate the out-of-the-box capabilities of GPT-4V in the domain of image forensics, specifically, in detecting image splicing manipulations. Without any task-specific fine-tuning, we evaluate GPT-4V using three prompting strategies: Zero-Shot (ZS), Few-Shot (FS), and Chain-of-Thought (CoT), applied over a curated subset of the CASIA v2.0 splicing dataset. Our results show that GPT-4V achieves competitive detection performance in zero-shot settings (more than 85% accuracy), with CoT prompting yielding the most balanced trade-off across authentic and spliced images. Qualitative analysis further reveals that the model not only detects low-level visual artifacts but also draws upon real-world contextual knowledge such as object scale, semantic consistency, and architectural facts, to identify implausible composites. While GPT-4V lags behind specialized state-of-the-art splicing detection models, its generalizability, interpretability, and encyclopedic reasoning highlight its potential as a flexible tool in image forensics.

Abstract:With the development and widespread application of digital image processing technology, image splicing has become a common method of image manipulation, raising numerous security and legal issues. This paper introduces a new splicing image detection algorithm based on the statistical characteristics of natural images, aimed at improving the accuracy and efficiency of splicing image detection. By analyzing the limitations of traditional methods, we have developed a detection framework that integrates advanced statistical analysis techniques and machine learning methods. The algorithm has been validated using multiple public datasets, showing high accuracy in detecting spliced edges and locating tampered areas, as well as good robustness. Additionally, we explore the potential applications and challenges faced by the algorithm in real-world scenarios. This research not only provides an effective technological means for the field of image tampering detection but also offers new ideas and methods for future related research.

In existing splicing forgery datasets, the insufficient semantic varieties of spliced regions cause a problem that trained detection models overfit semantic features rather than splicing traces. Meanwhile, because of the absence of a reasonable dataset, different detection methods proposed cannot reach a consensus on experimental settings. To address these urgent issues, GreatSplicing, a manually created splicing dataset with a considerable amount and high quality, is proposed in this paper. GreatSplicing comprises 5,000 spliced images and covers spliced regions with 335 distinct semantic categories, allowing neural networks to grasp splicing traces better. Extensive experiments demonstrate that models trained on GreatSplicing exhibit minimal misidentification rates and superior cross-dataset detection capabilities compared to existing datasets. Furthermore, GreatSplicing is available for all research purposes and can be downloaded from www.greatsplicing.net.