python python-3.x image image-processing computer-forensics

Finding a modified image - image forensics

I have a couple of standard ways of detecting a modified image such as

Luminance gradient
Copy move detection
Metadata Extraction
Histogram analysis
ELA(Error level analysis)
Quantization matrix analysis
Thumbnail analysis

are there any other standard ways of detecting a modified image?

Tried out

Finding the EXIF of the image to check the created and modified date and check for modification. I also had some rules for EXIF camera make and make note validation along with checking for the software used such as photoshop, Shotwell, etc.
Was able to segment the image and use SLIC(simple linear iterative clustering) to find out the similar cluster regions in an image
Find the largest contour with less pixel inconsistency with luminance gradient to mark that as a potential modified region
Largest contour with ELA as a potential modified region
Check for inconsistencies in histogram graph and mark it as a potential editted image.

Here are my questions

Are there any standard logics to verify the image with metadata such as using the created and modified dates, the camera make or maker note, etc. As these details are not consistent for any given image.
Finding out the least pixel inconsistency contour in the Luminance gradient would always give me an image that is modified?
If the histogram graph has a regular interval fluctuation could it be considered a modified image?
How could I use Quantization matrices to find out image anomalies
what is the best way to compare the thumbnail image to the original image to check for inconsistencies?

Solution

The answer to this question needs more detailed so, I will give some references to the subject itself and I will share with you the code of every part of your question :

You need to use exif to verify the image with metadata

For Anomaly Detection in Images see here

To compare the thumbnail image to the original image read this. where it showed you how to compare two images using Python.

References :