# A Survey of Detection and Mitigation for Fake Images on Social Media Platforms
## 4 Fake Image Detection Methods--Using Handcrafted Feature Set
### Forensic Features Based
#### 4.1.6 Problems--Forensic Method  
The survey presented various techniques used in forensic methods. Forensic methods are specialized methods to be used for some specific manipulation methods. When the images are shared over social networks, the shared image typically undergoes multiple manipulation and transformation. Thus, it becomes very challenging to exploit any one method for detection. High accuracy is observed in the single-manipulation process, but not much efficiency is achieved for multi-manipulation. For example, Figure 12 shows how tiny image manipulations are misclassified as fake using forensic techniques. When images are highly compressed, resized, and cropped, undergo arbitrary rotation, mirroring, and added noise for social media usage, it reduces the overall quality of images, making it hard to discriminate between real and fake. Nowadays, the virtual images generated via adversarial examples have significantly the same image features as the original, and thus, many algorithms fail to spot the tampering. GAN-made pictures are best detected when the deep learning CNN approach is applied along with a forensic approach.  
Table 5 illustrates performance results between various forensic feature-based techniques. Here, it is to be noted that only those techniques compared, which used \"accuracy\" as an evaluation parameter, and experiments were performed on similar image-specific datasets. It is obvious that with increasing knowledge and technology, the accuracy of the methods is improving, and the best accuracy is achieved by using machine learning with multiple features. However, as explained above, the forensic method's efficiency fails when the image has undergone multiple manipulations.  
The issues regarding forensic methods over social media images can be collated as below:  
* Specialization: Non-specialized examples as they undergo multiple manipulations;
* Proper resolution: Social Images are deficient in quality due to size constraints over the platforms;
* Compression: They are highly compressed images and have multiple compressions at times;
* Visual Features: Noise addition through the blur and edge removal techniques; thus, features are lost;
* Cropping: Much cropping is carried out to hide the details and highlight emotional content;
* Regions: Images can have large manipulated areas or tiny tampered patches. Figure 12 shows one such example, where a real tree shoot is marked as tampered (last right shoot);
* Source: Sources can be different, like digital cameras, computer-generated, and GAN;
* Formats: Platforms support multiple formats like JPEG, TIFF, GIF, BMP, PNG, and PSB.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Tampering Method** & **Detection Method** & **Columbia** & **CASIA 1.0** & **CASIA 2.0** \\ \hline [PERSON] et al. [30] & Copy-and-Move & MultiWLD & 94.17\% & 94.19\% & 96.61\% \\ \hline [PERSON] et al. [31] & Copy-and-Move & DCT+ KPCA & - & 92.62\% & 96.52\% \\ \hline [PERSON] et al. [43] & Image Splicing & GLCM + BFS & - & 90.50\% & - \\ \hline [PERSON] et al. [44] & Image Splicing & RLRN & 85.00\% & 94.70\% & - \\ \hline [PERSON] et al. [47] & Image Splicing & SVM + RFE & 93.55\% & - & 89.76\% \\ \hline [PERSON] et al. [49] & Image Splicing & SVD + DCT & 98.78\% & - & - \\ \hline [PERSON] et al. [50] & Image Splicing & DOCT + Markov & - & 98.77\% & 97.59\% \\ \hline [PERSON] et al. [51] & Both & DWT + HOG+ LBP + ML & 98.80\% & 98.30\% & 99.50\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy comparison of forensic methods.  
Figure 12: Tampered region identified by forensic technique [29].