International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Deep Fake Face Detection Using InceptionNet |
|---|---|
| ABSTRACT | The rapid advancement of generative artificial intelligence has led to the creation of highly realistic synthetic media, commonly known as deepfakes. These manipulated videos and images pose significant threats to digital security, privacy, and trust in online information. Malicious actors can exploit deepfake technology to spread misinformation, commit fraud, impersonate individuals, and undermine public confidence in digital content. Consequently, developing robust and reliable deepfake detection systems has become a critical research priority in the field of cybersecurity and digital forensics. This study proposes an intelligent framework for deep fake face detection using InceptionNet architecture integrated with Explainable Artificial Intelligence (XAI). The proposed framework analyzes facial images and video frames to identify manipulation artifacts, inconsistencies, and forgery patterns that indicate synthetic content. InceptionNet is utilized for extracting multi-scale spatial features and facial characteristics through its inception modules, which capture patterns at different resolutions simultaneously. The integration of Explainable AI enhances transparency by providing interpretable explanations for detection decisions, helping users understand which facial features or artifacts led to classification outcomes. The proposed system classifies media content as either Authentic or Deepfake and generates explainable reports that support digital forensic investigations. Experimental analysis demonstrates that the InceptionNet-based framework achieves high detection accuracy and effectively identifies forgery patterns in facial images and videos. The findings highlight the potential of intelligent deepfake detection systems for combating misinformation, protecting digital identity, and enhancing cybersecurity in the modern digital landscape. |
| AUTHOR | DR.T.V.S SRIRAM, S. JAYAPRADHA, SINDHU KAKARLA Sr. Asst. Professor, Dept. of MCA, NSRIT, Visakhapatnam, AP, India Asst. Professor, Dept. of MCA, NSRIT, Visakhapatnam, AP, India Dept. of MCA, NSRIT, Visakhapatnam, AP, India |
| VOLUME | 186 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1407063 |
| pdf/63_Deep Fake Face Detection Using InceptionNet.pdf | |
| KEYWORDS | |
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