Privacy considerations in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Data Anonymization in Computer Vision

Which of the following best describes data anonymization in the context of computer vision?

A Encrypting image files so only authorized users can view them

B Removing or obscuring personal identifiers in images to prevent tracing back to individuals

C Collecting more images to improve model accuracy without restrictions

D Sharing raw images with third parties for faster processing

💡 Hint

Think about how to protect personal identity in images.

Data anonymization means hiding or removing personal details in images so people cannot be identified, which protects privacy.

❓ Model Choice

intermediate

Choosing a Privacy-Preserving Model

You want to build a face recognition system but must ensure user privacy by not storing raw images. Which model approach is best?

A Use a cloud service that stores all images unencrypted

B Upload all raw images to a central server for training

C Train a model locally on user devices and only send encrypted feature vectors to the server

D Collect images without user consent to increase dataset size

💡 Hint

Consider how to keep raw images private while still training a model.

Training locally and sending only encrypted features keeps raw images private and reduces privacy risks.

❓ Metrics

advanced

Evaluating Privacy Leakage

Which metric would best measure how much private information a computer vision model unintentionally reveals?

A Membership inference attack success rate

B Model accuracy on test images

C Training loss value

D Number of model parameters

💡 Hint

Think about attacks that try to find if data was used in training.

Membership inference attack success rate measures how well an attacker can tell if a specific image was in the training set, indicating privacy leakage.

🔧 Debug

advanced

Identifying Privacy Risks in Code

Given this code snippet for a face detection pipeline, which line introduces a privacy risk?

import cv2

image = cv2.imread('user_photo.jpg')
faces = face_detector.detect(image)
cv2.imwrite('detected_faces.jpg', faces)
upload_to_server('detected_faces.jpg')

A cv2.imwrite('detected_faces.jpg', faces)

B face_detector.detect(image)

C cv2.imread('user_photo.jpg')

D upload_to_server('detected_faces.jpg')

💡 Hint

Consider what data is sent outside the local system.

Uploading detected faces to a server can expose personal data if not anonymized or encrypted, creating a privacy risk.

🧠 Conceptual

expert

Balancing Model Utility and Privacy

Which technique best balances maintaining model accuracy while protecting privacy in computer vision?

A Differential privacy applied during model training

B Removing all personal data and training on random noise

C Sharing raw images with all collaborators

D Training on a very small dataset to avoid leaks

💡 Hint

Think about adding controlled noise to protect data but keep useful patterns.

Differential privacy adds noise during training to protect individual data points while preserving overall model performance.

Practice

1. What is the main reason to blur faces in images used for computer vision projects?

easy

A. To make the images look artistic

B. To improve the image quality for better model training

C. To reduce the file size of the images

D. To protect people's privacy by hiding their identity

Solution

1. Step 1: Understand privacy protection in images

   Blurring faces hides personal identity, which protects privacy.

2. Step 2: Compare other options

   Improving quality, reducing size, or artistic effects do not relate to privacy.

3. Final Answer:

   To protect people's privacy by hiding their identity -> Option D

4. Quick Check:

   Blurring faces = privacy protection [OK]

Hint: Blurring hides identity to protect privacy [OK]

Common Mistakes:

• Thinking blurring improves image quality
• Confusing file size reduction with privacy
• Assuming artistic effects protect privacy

2. Which of the following is the correct way to remove metadata from an image file in Python?

easy

A. Use PIL's Image.save() with 'exif' parameter set to None

B. Use cv2.imread() and cv2.imwrite() without extra steps

C. Rename the image file extension to .txt

D. Open the image in a text editor and delete random lines

Solution

1. Step 1: Identify proper metadata removal method

   PIL's Image.save() with 'exif=None' removes metadata correctly.

2. Step 2: Evaluate other options

   cv2.imread/write does not remove metadata; renaming or editing text is invalid.

3. Final Answer:

   Use PIL's Image.save() with 'exif' parameter set to None -> Option A

4. Quick Check:

   Remove metadata = PIL save with exif=None [OK]

Hint: Use PIL save with exif=None to remove metadata [OK]

Common Mistakes:

• Assuming cv2.imwrite removes metadata
• Renaming file extensions changes nothing
• Editing image as text corrupts the file

3. Consider this Python code snippet that blurs faces in an image using OpenCV:

import cv2
image = cv2.imread('group_photo.jpg')
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
faces = face_cascade.detectMultiScale(image, scaleFactor=1.1, minNeighbors=5)
for (x, y, w, h) in faces:
    face_region = image[y:y+h, x:x+w]
    blurred_face = cv2.GaussianBlur(face_region, (99, 99), 30)
    image[y:y+h, x:x+w] = blurred_face
cv2.imwrite('blurred_photo.jpg', image)

What will be the result of running this code?

medium

A. The output image will have all detected faces blurred to protect privacy

B. The output image will be unchanged because GaussianBlur is not applied correctly

C. The code will raise an error because detectMultiScale requires a grayscale image

D. The code will blur the entire image instead of just faces

Solution

1. Step 1: Trace the code execution

   cv2.imread loads a color image. However, detectMultiScale requires a grayscale image input, so passing a color image will cause an error or incorrect detection.

2. Step 2: Correct usage

   The image should be converted to grayscale before calling detectMultiScale, e.g., gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY).

3. Final Answer:

   The code will raise an error because detectMultiScale requires a grayscale image -> Option C

4. Quick Check:

   detectMultiScale requires grayscale input [OK]

Hint: detectMultiScale needs grayscale image [OK]

Common Mistakes:

• Thinking detectMultiScale works directly on color images
• Assuming no error on color input
• Believing blur applies to whole image

4. You have a dataset of images with faces but forgot to get consent from people. Which fix below best respects privacy and legal rules?

medium

A. Blur all faces in the dataset before using it for training

B. Use the images as is because they are publicly available

C. Remove all images with faces and keep only background images

D. Add random noise to images without blurring faces

Solution

1. Step 1: Identify privacy and legal requirements

   Consent is needed; without it, faces must be anonymized.

2. Step 2: Evaluate options for compliance

   Blurring faces anonymizes identities; using images as is or adding noise does not protect privacy properly.

3. Final Answer:

   Blur all faces in the dataset before using it for training -> Option A

4. Quick Check:

   No consent = anonymize faces by blurring [OK]

Hint: No consent? Blur faces to protect privacy [OK]

Common Mistakes:

• Assuming public availability means consent
• Thinking noise addition protects identity
• Removing images may lose valuable data unnecessarily

5. You want to build a face recognition system but must comply with privacy laws. Which combined approach best balances functionality and privacy?

hard

A. Train on unblurred public images and delete them after training

B. Collect images only with explicit consent and blur faces in public datasets

C. Use any available images without consent but encrypt the dataset

D. Avoid face recognition and use only object detection instead

Solution

1. Step 1: Understand privacy law requirements

   Explicit consent is required to use personal images legally.

2. Step 2: Combine consent and anonymization

   Blurring faces in public datasets protects privacy while allowing training.

3. Step 3: Evaluate other options

   Using images without consent or deleting after training does not ensure compliance; avoiding face recognition limits functionality.

4. Final Answer:

   Collect images only with explicit consent and blur faces in public datasets -> Option B

5. Quick Check:

   Consent + blur = privacy compliance and functionality [OK]

Hint: Consent plus blurring balances privacy and use [OK]

Common Mistakes:

• Thinking encryption replaces consent
• Assuming deleting data after training is enough
• Avoiding face recognition is not always necessary