UNDERCODE COMMUNITY
2.68K subscribers
1.23K photos
31 videos
2.65K files
80.4K links
🦑 Undercode Cyber World!
@UndercodeCommunity


1️⃣ World first platform which Collect & Analyzes every New hacking method.
+ AI Pratice
@Undercode_Testing

2️⃣ Cyber & Tech NEWS:
@Undercode_News

3️⃣ CVE @Daily_CVE

Web & Services:
Undercode.help
Download Telegram
Forwarded from Exploiting Crew (Pr1vAt3)
🦑 Setup a 2024 Drupal vulnerability:

After installing the drupal application the developer can change the hash_salt variable on line 268 in the /sites/default/settings.php file. As advised in the example the developer can use a file for this by using the file_get_contents function:
Forwarded from Exploiting Crew (Pr1vAt3)
If this is set to a file with a salt string in it the application works as intended. However if this is set to an empty file or a file that doesn’t exist. It could be that this file it was set to gets deleted, removed or renamed after some time. This will break the website. As an example the following code can be used as recommended in the image
Forwarded from Exploiting Crew (Pr1vAt3)
Now if this file gets removed the application breaks and the full path traversal is shown by navigating to /core/authorize.php as is shown in the image.
Forwarded from Exploiting Crew (Pr1vAt3)
🦑xx Deep Fake Videos Notes :

---

How Deepfakes Are Created:

#### 1. Understanding Deepfakes
Deepfakes are AI-generated videos or images where someone's face or voice is swapped or synthesized using machine learning techniques. They are often made with deep learning algorithms, particularly Generative Adversarial Networks (GANs) or autoencoders.

---

#### 2. Tools and Technologies Used
Creating deepfakes involves the following tools:

- DeepFaceLab: The most popular open-source tool for creating deepfake videos.
- FaceSwap: Another open-source tool with a user-friendly interface.
- First-Order Motion Model: Generates animations using reference images and motion vectors.
- DeepFakeGenerator: Often used for quick results with less control over quality.

---

#### 3. The Process
1. Collect Data:
- Gather high-quality video/images of the target (person to fake).
- More data = better quality. Include various angles and expressions.

2. Preprocessing:
- Detect and extract the face from all frames using facial recognition tools.
- Tools like dlib, MTCNN, or OpenCV can help with this.
- Align and normalize the faces to make them consistent.

3. Train the Model:
- Use an autoencoder or GAN-based model to train on the extracted faces.
- The model learns to replicate and map the source face onto the target face.
- Training can take days or weeks, depending on hardware and dataset size.

4. Face Swapping:
- Once trained, the model generates fake frames with the source face mapped onto the target video.

5. Postprocessing:
- Use video editing tools to blend the fake face seamlessly with the target.
- Adjust lighting, shadows, and other effects for realism.

6. Generate the Final Output:
- Combine the processed frames back into a video.

---

#### 4. Ethical Considerations
The challenge might also require discussing:
- The risks of misuse, such as in disinformation or explicit content.
- Legal and ethical implications.
- Techniques to detect and prevent deepfakes (e.g., AI-based detection algorithms).

---

#### Sample Code for Basics
Here's a simplified Python example using OpenCV to detect and align faces:

import cv2

# Load pre-trained face detection model
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# Read the image
image = cv2.imread('source_image.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Detect faces
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)

# Draw rectangles around faces
for (x, y, w, h) in faces:
cv2.rectangle(image, (x, y), (x+w, y+h), (255, 0, 0), 2)

cv2.imshow('Detected Faces', image)
cv2.waitKey(0)
cv2.destroyAllWindows()


---

### Key Points to Include
- Explain how GANs or autoencoders are essential for deepfake creation.
- Provide examples of tools like DeepFaceLab or code snippets (if allowed).
- Address ethical challenges.

This guide is for educational purposes only.🙂