Splicing Detection

Splicing detection focuses on identifying manipulated data—whether genetic sequences, audio recordings, or images—where segments from different sources have been combined. Current research emphasizes developing robust and efficient algorithms, often employing deep learning architectures like Transformers and convolutional neural networks, to detect these manipulations, even under challenging conditions such as compression or the absence of metadata. This field is crucial for maintaining data integrity across various domains, from forensic investigations and combating misinformation to ensuring the reliability of genomic data analysis. The development of self-supervised learning methods is a significant trend, aiming to reduce reliance on extensive labeled datasets.