Splice detection models are the need of the hour since splice manipulations can be used to mislead, spread rumors and create disharmony in society. However, there is a severe lack of image-splicing datasets, which restricts the capabilities of deep learning models to extract discriminative features without overfitting. This manuscript presents twofold contributions toward splice detection. Firstly, a novel splice detection dataset is proposed having two variants. The two variants include spliced samples generated from code and through manual editing. Spliced images in both variants have corresponding binary masks to aid localization approaches. Secondly, a novel spatio-compression lightweight splice detection framework is proposed for accurate splice detection with minimum computational cost. The proposed dual-branch framework extracts discriminative spatial features from a lightweight spatial branch. It uses original resolution compression data to extract double compression artifacts from the second branch, thereby making it ‘information preserving.’ Several CNNs are tested in combination with the proposed framework on a composite dataset of images from the proposed dataset and the CASIA v2.0 dataset. The best model accuracy of 0.9382 is achieved and compared with similar state-of-the-art methods, demonstrating the superiority of the proposed framework.

剪接检测模型是当前的需要，因为剪接操作可能被用来误导、传播谣言并在社会上制造不和谐。然而，图像拼接数据集的严重缺乏，限制了深度学习模型在不过度拟合的情况下提取判别特征的能力。这篇手稿对剪接检测做出了双重贡献。首先，提出了一种具有两种变体的新颖的剪接检测数据集。这两个变体包括通过代码和手动编辑生成的拼接样本。两种变体中的拼接图像都有相应的二进制掩模来帮助定位方法。其次，提出了一种新颖的空间压缩轻量级拼接检测框架，以最小的计算成本进行准确的拼接检测。所提出的双分支框架从轻量级空间分支中提取有区别的空间特征。它使用原始分辨率压缩数据从第二个分支中提取双重压缩伪影，从而使其“信息保留”。在来自提议数据集和 CASIA v2.0 数据集的图像复合数据集上，结合提议的框架对多个 CNN 进行了测试。实现了 0.9382 的最佳模型精度，并与类似的最先进方法进行了比较，证明了所提出框架的优越性。