Why responsible CV prevents misuse in Computer Vision - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load an image using OpenCV.

Computer Vision

import cv2
image = cv2.[1]('photo.jpg')

Drag options to blanks, or click blank then click option'

Aimshow

Bimread

Cimwrite

Dresize

Attempts:

3 left

💡 Hint

Common Mistakes

Using imshow instead of imread

Trying to write the image before loading it

The cv2.imread function reads an image from a file, which is the first step in computer vision tasks.

2fill in blank

medium

Complete the code to convert the image to grayscale.

Computer Vision

gray_image = cv2.cvtColor(image, [1])

Drag options to blanks, or click blank then click option'

Acv2.COLOR_BGR2GRAY

Bcv2.COLOR_BGR2RGB

Ccv2.COLOR_RGB2BGR

Dcv2.COLOR_GRAY2BGR

Attempts:

3 left

💡 Hint

Common Mistakes

Using COLOR_BGR2RGB which changes color space but not to grayscale

Using COLOR_GRAY2BGR which is the opposite conversion

To convert a color image to grayscale, use cv2.COLOR_BGR2GRAY as the color conversion code.

3fill in blank

hard

Fix the error in the code to display the image correctly.

Computer Vision

cv2.[1]('Image Window', gray_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

Drag options to blanks, or click blank then click option'

Aimwrite

Bimread

Cimshow

Dresize

Attempts:

3 left

💡 Hint

Common Mistakes

Using imread instead of imshow

Trying to write the image instead of displaying it

The cv2.imshow function displays an image in a window. Using imread or imwrite here would cause errors.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps image names to their sizes if width is greater than 100.

Computer Vision

image_sizes = {name: image.shape[[1]] for name, image in images.items() if image.shape[[2]] > 100}

Drag options to blanks, or click blank then click option'

A1

B0

C2

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing width and height indexes

Using the same index for both blanks

In OpenCV images, shape[1] is width and shape[0] is height. We want width > 100, so use shape[1] for size and shape[0] for height if needed.

5fill in blank

hard

Fill all three blanks to filter images with more than 3 channels and create a new dictionary with their names and channel counts.

Computer Vision

filtered = {name: image.shape[[1]] for name, image in images.items() if image.shape[[2]] > [3]

Drag options to blanks, or click blank then click option'

A2

B1

C3

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using height or width index instead of channels

Checking for channels < 3 instead of > 3

The third dimension (index 2) in image shape is the number of channels. We check if channels > 3 to filter images.

Practice

1. Why is responsible computer vision important in AI applications?

easy

A. It helps protect people's privacy and prevents unfair treatment.

B. It makes the computer vision models run faster.

C. It reduces the cost of hardware needed for training.

D. It guarantees 100% accuracy in image recognition.

Solution

1. Step 1: Understand the goal of responsible computer vision

   Responsible computer vision aims to avoid harm by protecting privacy and fairness.

2. Step 2: Compare options with this goal

   Only It helps protect people's privacy and prevents unfair treatment. mentions privacy and fairness, which matches the goal.

3. Final Answer:

   It helps protect people's privacy and prevents unfair treatment. -> Option A

4. Quick Check:

   Responsible CV = Protect privacy and fairness [OK]

Hint: Focus on privacy and fairness to spot the right answer [OK]

Common Mistakes:

• Confusing speed or cost with responsibility
• Thinking accuracy alone defines responsibility
• Ignoring privacy concerns

2. Which of the following is a correct practice in responsible computer vision?

easy

A. Anonymizing faces to protect identity

B. Collecting data without consent

C. Hiding model decisions from users

D. Ignoring data bias during training

Solution

1. Step 1: Identify responsible data handling practices

   Responsible CV includes protecting identities, such as anonymizing faces.

2. Step 2: Evaluate each option

   Only Anonymizing faces to protect identity describes anonymizing faces, which protects privacy.

3. Final Answer:

   Anonymizing faces to protect identity -> Option A

4. Quick Check:

   Anonymize data = Responsible practice [OK]

Hint: Look for privacy protection steps like anonymization [OK]

Common Mistakes:

• Choosing options that ignore consent or bias
• Thinking hiding info is responsible
• Confusing ignoring bias with responsibility

3. Consider this code snippet for a face recognition system:

def check_responsibility(data):
    if not data.get('consent'):
        return 'Reject data'
    if data.get('faces') and not data.get('anonymized'):
        return 'Anonymize faces'
    return 'Data accepted'

result = check_responsibility({'consent': True, 'faces': True, 'anonymized': False})
print(result)

What will be printed?

medium

A. "Reject data"

B. "Anonymize faces"

C. "Data accepted"

D. Error due to missing keys

Solution

1. Step 1: Check consent key in data

   Consent is True, so it does not return 'Reject data'.

2. Step 2: Check faces and anonymized keys

   Faces is True and anonymized is False, so it returns 'Anonymize faces'.

3. Final Answer:

   "Anonymize faces" -> Option B

4. Quick Check:

   Faces present + not anonymized = Anonymize faces [OK]

Hint: Follow the if conditions step-by-step [OK]

Common Mistakes:

• Ignoring the anonymized check
• Assuming missing keys cause error
• Confusing consent True with False

4. The following code is intended to check if data is responsibly handled by verifying consent and anonymization. What is the bug?

def validate_data(data):
    if data['consent'] == False:
        return 'Reject data'
    if data['faces'] and data['anonymized'] == False:
        return 'Anonymize faces'
    return 'Data accepted'

print(validate_data({'consent': True, 'faces': True, 'anonymized': False}))

medium

A. Function does not return any value

B. Using '==' instead of 'is' for boolean checks

C. Incorrect logic: should check if anonymized is True

D. Missing key checks may cause KeyError

Solution

1. Step 1: Analyze key access in the code

   The code accesses data['consent'], data['faces'], and data['anonymized'] directly without checking if keys exist.

2. Step 2: Understand potential errors

   If any key is missing, a KeyError will occur, causing a crash.

3. Final Answer:

   Missing key checks may cause KeyError -> Option D

4. Quick Check:

   Direct key access without checks risks KeyError [OK]

Hint: Check if keys exist before accessing dictionary values [OK]

Common Mistakes:

• Thinking '==' vs 'is' causes bugs here
• Assuming logic is reversed
• Ignoring possibility of missing keys

5. A company wants to build a computer vision system that detects people in images but must avoid misuse by protecting privacy and fairness. Which combination of practices best supports responsible CV?

hard

A. Use only high-resolution images and skip consent to speed up training.

B. Train on biased data but hide model details to prevent misuse.

C. Collect diverse data, anonymize faces, and explain model decisions clearly.

D. Ignore data diversity and focus on maximizing accuracy only.

Solution

1. Step 1: Identify responsible CV practices

   Responsible CV requires diverse data to avoid bias, anonymization to protect privacy, and transparency to build trust.

2. Step 2: Evaluate options against these practices

   Only Collect diverse data, anonymize faces, and explain model decisions clearly. includes all these: diverse data, anonymization, and clear explanations.

3. Final Answer:

   Collect diverse data, anonymize faces, and explain model decisions clearly. -> Option C

4. Quick Check:

   Diversity + privacy + transparency = Responsible CV [OK]

Hint: Pick options covering privacy, fairness, and transparency [OK]

Common Mistakes:

• Ignoring data diversity
• Skipping consent or anonymization
• Thinking accuracy alone ensures responsibility