Malware Detection Based on Image
Author(s): 1.Mr. Prince Bodarya , 2. Ms. Naisargi Parikh, 3.Mr. Dhruv Solanki, 4.Ms. Mahi Darji , 5. Ms. Manisha Vasava
Authors Affiliations:
1,2,3,4 Research Scholar, Department of Information Technology, Krishna School of Emerging Technology& Applied Research, KPGU University, Varnama, Vadodara, Gujarat, India
5. Assistant Professor, Department of Information Technology, Krishna School of Emerging Technology & Applied Research, KPGU University, Varnama, Vadodara, Gujarat, India
DOIs:10.2015/IJIRMF/202510007 | Paper ID: IJIRMF202510007This work presents a Python-based tool for detecting malware are present or not. Its safety of common image formats—GIF (Graphics Interchange Format), PNG (Portable Network Graphic.), and JPEG (Joint Photographic Experts Group) to detect potentially malformed or unsafe files. The program performs a low-level byte and hex analysis of image files to ensure compliance with format specifications. For GIF files, it verifies headers, global and local colour tables, extension blocks, and trailer markers. For JPEG files, it checks for the correct start and end markers and ensures no extra data exists after the trailer. For PNG files, the script performs a detailed inspection of critical chunks , verifying CRC (Cyclic Redundancy Check) checksums to confirm data integrity. The tool is designed to scan a folder of images, classify each as safe or unsafe, and export results into a CSV file for further analysis. This ensures data safety, integrity, and robustness when handling untrusted or externally sourced image files, which could otherwise be exploited for vulnerabilities
Mr. Prince Bodarya , Ms. Naisargi Parikh, Mr. Dhruv Solanki, Ms. Mahi Darji , Ms. Manisha Vasava (2025); Malware Detection Based on Image, International Journal for Innovative Research in Multidisciplinary Field, ISSN(O): 2455-0620, Vol-11, Issue-10, Pp. 53-60. Available on – https://www.ijirmf.com/
[1] Rabia Tahir. A Study on Malware and Malware Detection Techniques [2018]
[2] LailacTul Badar, Barbara Carminati, Elena Ferrari. A comprehensive survey on stego-malware detection in digital media, research challenges and future directions [2025]
[3] A. Bensaoud, J. Kalita, M. Bensaoud. A survey of malware detection using deep learning [2024]
[4] Sebastian Zillien, Denis Petrov, Pascal Ruffing, & Friedrich Gros. A Development Framework for TCP/IP Network Steganography Malware Detection [2024]
[5] Shruti Dhumal, Gayatri Gutte, Shreya Kondalka, Tanuja Kale, Shobha Raskar. Spiteful JPEG: Machine Learning Based Solution for the Detection of Malware in JPEG Images and Image Forgery [2024]
[6] Jamal El Abdelkhalki, Mohamed Ben Ahmed, Boudhir Anouar Abdelhakim. Image Malware Detection using Deep Learning [2020]
[7] Anni Karimatul Fauziyyah, Ronald Adrian, Sahirul Alam. Analyzing Image Malware with OSINTs after Steganography using Symmetric Key Algorithm [2023]
[8] Iman Almomani, Aala AlKhayer, Walid El-Shafai. Novel Ransomware Hiding Model Using HEVC Steganography Approach [2021]
[9] Li Jia, Anmin Zhou, Peng Jia, Luping Liu, Yan Wang, Liang Liu. A Neural Network-Based Approach for Cryptographic Function Detection in Malware. [2020]
[10] S.H. Kok, Azween Abdullah, NZ Jhanjhi. Early detection of crypto-ransomware using pre-encryption detection algorithm [2020]
[11] V. Krithika, M. Vijaya, Malware detection using gist features and deep neural network. [2020]
[12] H.D. Samuel, M.S. Kumar, R. Aishwarya, G. Mathivanan, Automation detection of malware and stenographical content using machine learning. [2022]
[13] A. Monika, R. Eswari, Prevention of hidden information security attacks by neutralizing stego-malware[2022]
[14] M. Płachta, M. Krzemień, K. Szczypiorski, A. Janicki, Detection of image steganography using deep learning and ensemble classifiers. [2022]
[15] M. Guarascio, M. Zuppelli, C. Nunziato, C. Luca, M. Giuseppe, Revealing MageCart-like threats in favicons via artificial intelligence[2022]
[16] C. Nunziato, C. Luca, G. Massimo, M. Giuseppe, Z. Marco, Detection of steganographic threats targeting digital images in heterogeneous ecosystems through machine learning[2022]