An Attention Network for Detection of Spliced Video Objects Inspired by Manipulated Visual Social Media Privacy Sensitive Issues Using NV2CIR Dataset

Even though forgery detection is a well matured topic, reporting on the same for detection of forgery in night time outdoor scenes is not explored to date. Despite the lack of appropriate benchmark video datasets for forgery detection at night time using infrared (IR) video modality, we designed a novel ground truth annotated forged video dataset named as “NV2CIR (Night Vision Video Forensic Challenges based IR Forged Dataset)” in real-world night time situations. The dataset contains various 310 infrared imaging based forged videos (i.e., object based forgery, interframe forgery, and intraframe forgery) and their corresponding 310 authentic videos. The article also proposed a novel framework named as “AFOD-Net (attention guided fake object detection network)” for localization of spliced objects in night vision outdoor scenarios. The proposed network employs a long short term memory (LSTM) module and our proposed spliced object attention module (SOA) so as to precisely localize the spatio-temporal spliced regions. Experimental results show that AFOD-Net significantly improves the performance for localizing the spliced objects on our designed spliced video dataset with mean average precision (mAP) of 85.16%.