Digital Image Forensics: My Approach to Detecting Image Manipulation

In today’s digital world, anyone can alter an image with just a few clicks. Because of this, I wanted my digital forensics project to focus on a real investigative challenge: how can we scientifically determine whether an image is authentic or manipulated? Using a combination of forensic tools and structured analysis, I examined how metadata, compression signatures, pixel inconsistencies, and cloned regions expose tampered photographs. The posters I created visually summarize this workflow, and this blog expands on the technical work I personally carried out during the project.

This is my poster design project so you may find some posters in this blog.

1. Why I Chose Image Manipulation Detection

I chose this topic because image tampering plays a major role in misinformation, cybercrime, evidentiary fraud, and digital deception. Simply looking at an image is no longer sufficient—modern editing tools and AI-based generators can produce images that appear completely realistic. I wanted to design a methodology that relies on technical evidence rather than subjective judgment. My objective was to analyze images in a way that could be trusted by investigators, journalists, or even a court of law.

2. How I Used Metadata Analysis with ExifTool

The first step in my analysis was extracting metadata using ExifTool. Metadata is the hidden information embedded by a camera, such as timestamps, GPS coordinates, camera make and model, and exposure settings. During my analysis, I observed that genuine images typically contain rich and consistent metadata, while edited images often show missing fields or traces of editing software.

For example, when I analyzed certain sample files, ExifTool revealed entries such as:

Software: Adobe Photoshop

even though the image was claimed to be captured directly from a mobile phone. In other cases, important metadata such as timestamps or camera information was completely missing. These inconsistencies immediately raised suspicion. By running a simple command like:

exiftool suspect.jpg

I was able to determine whether an image had been processed or altered before further analysis. Metadata alone does not prove manipulation, but it serves as a critical first indicator.

3. How I Verified Authenticity Through JPEG Compression (JPEGsnoop)

After metadata analysis, I moved on to compression analysis using JPEGsnoop, which was one of the most technical steps in my workflow. JPEGsnoop examines quantization tables embedded inside JPEG images. These tables are usually unique to specific camera models, meaning an image taken directly from a camera should match a known compression signature.

During my tests, I encountered images labeled as “original” that actually contained compression signatures associated with image-editing software instead of a physical camera. JPEGsnoop flagged these files as processed or edited, confirming that the images had been modified at some point. This step was particularly valuable because compression signatures are much harder to fake than metadata. It allowed me to validate authenticity at the file-structure level rather than relying on visual appearance.

4. How I Used Error Level Analysis (ELA) to Identify Altered Regions

Next, I applied Error Level Analysis (ELA) using FotoForensics. ELA works by highlighting differences in compression error levels across an image. In a genuine image, compression errors are generally uniform. However, when an image is edited—such as when an object is added, removed, or altered—the modified regions often show different error patterns.

During my project, I found that even when edits were invisible to the naked eye, ELA revealed them clearly through brighter or irregular regions. In one case, an image appeared completely normal until ELA exposed abnormal brightness around a specific object, indicating it had been inserted. When I intentionally tested object insertion myself, the pasted region consistently stood out in the ELA heatmap compared to the original background. This confirmed ELA as one of the most effective techniques in my forensic methodology.

5. How I Used Clone Detection and Magnification in Forensically

After identifying suspicious regions using ELA, I used Forensically to perform deeper pixel-level analysis. Clone detection helped me identify repeated pixel patterns, which commonly occur when clone stamp tools are used to remove or duplicate elements within an image.

In several images I analyzed, clone detection highlighted identical texture regions—such as grass or background elements—appearing more than once in unnatural ways. This strongly indicated manipulation. I also used the magnification tool to closely inspect edges, shadows, and transitions between objects. When objects were artificially pasted into a scene, magnification revealed subtle issues such as unnatural edge softness, halo artifacts, or lighting inconsistencies that were not visible at normal zoom levels. These micro-level observations played a crucial role in confirming tampering.

6. Real vs. Fake Examples I Analyzed

In the final stage of my project, I analyzed multiple pairs of real and manipulated images. Some manipulations were extremely subtle—objects were duplicated, backgrounds were replaced, or additional elements were added to enhance the visual impact. In one example, a dramatic background element was artificially inserted to change the context of the image. In another, reflections and visual details were altered to give a misleading impression.

By applying my four-step forensic process to these images, I found that even highly realistic manipulations failed at least one detection layer. Some images showed missing metadata, others failed compression signature checks, while ELA revealed hidden edits and clone detection exposed duplicated textures. These examples demonstrated why relying on a single technique is insufficient and why a layered forensic approach is necessary.

Answer: Both are edited

Answer: white bunny is added- look at the leg and grass part- it doesn't feel real.

Answer - Obviously, you know.

Answer - Obviously, you know.

Answer - The right one is edited

Really? Don't believe what I say - the Left one is edited.

7. The Workflow I Developed for Image Authentication

Based on my analysis, I developed a structured workflow for image authentication:

• Metadata extraction using ExifTool

• Compression signature analysis using JPEGsnoop

• Error Level Analysis using FotoForensics

• Pixel-level manipulation detection using Forensically

By correlating findings from all four methods, I was able to build strong, defensible conclusions about whether an image was authentic or manipulated. This workflow closely mirrors real-world practices used in journalism verification, legal investigations, and digital forensic examinations.

8. Conclusion: What I Learned from This Project

Through this project, I learned that digital image forensics is not about guessing—it is about collecting and correlating measurable digital evidence. I gained a deeper understanding of how easily images can be manipulated and how difficult those manipulations are to detect without the right tools. By applying ExifTool, JPEGsnoop, FotoForensics, and Forensically, I developed a reliable, repeatable approach to uncover image tampering that the human eye alone cannot detect. This project strengthened my forensic mindset and prepared me to analyze manipulated media with greater confidence in real-world investigations.