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@article{196353,
        author = {Kaicharla Chandrika and Narayanasetti Akshitha and Konduri Chaitanya and Pilli Naveen and Dr.K.V. SATYANARAYANA},
        title = {DIGITAL IMAGE STEGANALYSIS FOR DETECTION OF CONCEALED INFORMATION USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {3163-3168},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196353},
        abstract = {Steganography sneaks’ secret info into digital images so sneaky, the human eye misses it completely. It's great for secure chats, but a nightmare for cybersecurity—think hidden data leaks or sneaky transmissions. Spotting that concealed stuff? Digital forensics' holy grail. 
This paper rolls out a smart, automated steganalysis system powered by machine learning to catch images hiding data. We run a straightforward pipeline: preprocess images, extract features from all angles (noise residuals, LSB patterns, DCT frequencies, GLCM textures—241 in total), trim with PCA (keeping 99% variance), then classify via SVM with RBF kernel. 
Trained on 4,000 images, tested on 800, it hits 83.4% accuracy, 80.5% precision, 88.0% recall, and 84.1% F1-score. That killer recall means it nabs most stego images—cybersecurity win. Bonus: Streamlit web app for instant, realtime checks.},
        keywords = {Steganalysis, Steganography, Machine Learning, Support Vector Machine (SVM), Least Significant Bit (LSB), Image Processing, Cybersecurity, Digital Forensics.},
        month = {April},
        }