TL;DR
Qualitative analyses show that Grad-CAM captures localized manipulation artifacts, whereas Attention Rollout models broader contextual relationships, highlighting the complementary strengths of the proposed explainability-guided local–global inference framework for trustworthy deepfake detection.
The rapid growth of deepfake videos has become a significant threat to the believability of visual media, enabling misinformation, manipulation, and damage on a large scale. Even though recent detectors using deep learning methods exhibit high accuracy in identifying deepfake videos, these detectors are often black box...
Now a days identification of convincing synthetic videos created with the help of deepfake is a big challenge. Unfortunately, deepfakes represent a serious threat to the integrity of media, as they can cause individuals to lose trust in the digital content they see. Among all types of deepfakes, face-swap videos are ex...
The rapid spread of manipulated media, particularly deepfake audio and video, poses a significant threat to information integrity on digital platforms. To address this challenge, a web-based detection system called IntegriFy is developed using artificial intelligence techniques. The system integrates a Random Forest cl...
This paper presents a robust deepfake image detection framework using the Swin-B Transformer, a pre-trained model fine-tuned for this application, and visualize the spatial segments of the image that the model focuses on during classification, providing insight into the decision-making process.
Detecting audio-visual DeepFake (AVDeepFake) is becoming increasingly important as synthetic media tools become widely accessible and spread across consumer devices. In this study, we present an Detecting audio-visual DeepFakes (AV-DeepFakes) has become increasingly critical with the rapid proliferation of accessible s...
This research develops a novel method that incorporates a robust spatiotemporal learning model that uses an Extreme Inception Network (XceptionNet) architecture to extract spatial features, a Bidirectional Long Short-Term Memory (BiLSTM) network for temporal sequence learning, and a temporal attention mechanism for ada...
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