Aadya Singh — Published On September 26, 2023
Artificial Intelligence Beginner Data Visualization Generative AI Machine Learning Videos


Generative AI in forensic science involves applying artificial intelligence techniques to generate data, images, or other forensic evidence-related information. This technology can potentially revolutionize forensic science by aiding investigators in tasks such as image and video analysis, document forgery detection, crime scene reconstruction, and more. Forensic science has long relied on meticulous examination of physical evidence to solve crimes. However, with the rapid advancement of technology, the field has embraced artificial intelligence (AI) and, more specifically, generative AI to enhance its capabilities. In this article, we will explore the practical applications of generative AI in forensic science and provide code implementations for some of these applications.

 Image : https://www.analyticsinsight.net/wp-content/uploads/2021/08/AI-in-Forensic-Investigation-and-Crime-Detection.jpg

Learning Objectives

  1. Learn how generative AI, including GANs and Siamese networks, can be applied in practical scenarios within forensic science.
  2. How to implement key generative AI algorithms for image reconstruction, fingerprint recognition, and document forgery detection.
  3. Understand the ethical considerations surrounding the use of AI in forensic investigations, including data privacy and fairness concerns.

This article was published as a part of the Data Science Blogathon.

Enhancing Image Reconstruction with Generative AI

Enhancing Image Reconstruction with Generative AI has emerged as a groundbreaking advancement in forensic science, offering a transformative approach to image analysis and evidence interpretation. Research papers in this domain have highlighted the significant potential of Generative Adversarial Networks (GANs) and other generative AI techniques in the field. These innovative technologies have enabled forensic investigators to reconstruct and enhance images obtained from various sources, including surveillance cameras, low-resolution photographs, or pixelated images, providing invaluable support in criminal investigations.

Generative AI, particularly GANs, leverages a dual-network architecture comprising a generator and discriminator to generate realistic, high-quality images. By training on a dataset of diverse images, GANs learn to understand patterns, textures, and structures in visual data. In forensic science, this technology has enabled experts to unveil critical details from blurry, fragmented, or incomplete images. Additionally, GANs have been employed in facial recognition and composite sketch generation, aiding law enforcement agencies in identifying potential suspects more accurately. Reconstructing crime scenes and generating missing evidence has also revolutionized the investigative process, allowing for more comprehensive and data-driven analysis. As research in Enhancing Image Reconstruction with Generative AI advances, the potential for solving complex criminal cases and ensuring justice becomes increasingly promising, solidifying its role as a transformative force in modern forensic science.

 Image Credits : https://aihubprojects.com/forensic-sketch-to-image-generator-using-gan/

Image Super-Resolution with GANs

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, UpSampling2D

model = Sequential()
model.add(Conv2D(64, (3, 3), activation='relu', padding='same', input_shape=(64, 64, 3)))
model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))
model.add(UpSampling2D((2, 2)))
model.add(Conv2D(3, (3, 3), activation='sigmoid', padding='same'))

model.compile(optimizer='adam', loss='mse')
model.fit(training_data, target_data, epochs=100, batch_size=32)

Automated Fingerprint Recognition

Fingerprint analysis is a cornerstone of forensic investigations. Generative AI can streamline the process by automating fingerprint recognition tasks.

Automated fingerprint recognition has undergone a transformative evolution in forensic science, and the integration of Generative AI techniques has played a pivotal role in enhancing its accuracy and efficiency. Generative Adversarial Networks (GANs) have emerged as a groundbreaking approach in this domain, enabling the creation of synthetic fingerprint images and refining existing fingerprint databases. Through GANs, researchers can generate highly realistic fingerprint samples, facilitating the augmentation of training datasets and improving the robustness of fingerprint recognition algorithms. This innovation addresses the inherent challenges in traditional fingerprint recognition systems, such as limited datasets and the susceptibility to variations in fingerprint quality due to factors like distortion, smudging, or partial prints.

Moreover, Generative AI has significantly advanced the capabilities of fingerprint image enhancement and restoration. By leveraging techniques like convolutional neural networks (CNNs) and autoencoders, researchers have restored and clarified latent fingerprint images from complex backgrounds, even in cases of degraded or partial prints. This breakthrough has been instrumental in uncovering critical evidence from crime scenes and latent prints that were previously deemed unusable. Additionally, Generative AI-based approaches have enabled the creation of high-resolution fingerprint representations, enhancing the minutiae extraction process and contributing to the precision of matching algorithms.

Fingerprint Matching with Siamese Networks

# Import necessary libraries
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Flatten, Dense, Lambda

# Define a Siamese network for fingerprint matching
input_shape = (128, 128, 1)
input_left = Input(shape=input_shape)
input_right = Input(shape=input_shape)

base_network = Sequential([
    Conv2D(64, (3, 3), activation='relu', input_shape=input_shape),
    Dense(128, activation='relu')

output_left = base_network(input_left)
output_right = base_network(input_right)

# Calculate L1 distance between embeddings
distance = Lambda(lambda x: tf.abs(x[0] - x[1]))([output_left, output_right])
similarity_score = Dense(1, activation='sigmoid')(distance)

# Build and compile the Siamese model
siamese_model = Model(inputs=[input_left, input_right], outputs=similarity_score)
siamese_model.compile(optimizer='adam', loss='binary_crossentropy')

Document Forgery Detection

In recent years, the advancement of Generative Adversarial Networks (GANs) has given rise to a concerning technology – digital image forgery. This technology has reached a level where it is nearly impossible to distinguish a forged image from an original one with the naked eye. From compositing and editing faces to altering entire documents, the implications of digital image forgery are far-reaching, posing a significant social challenge. Document forgery, in particular, can potentially manipulate the meaning and context of critical documents, making it extremely difficult to discern authenticity.

Despite the growing threat of digital forgery, there has been a noticeable lack of research in the field, even as new forgery-related attacks emerge daily. This study introduces a groundbreaking solution to address this gap: a novel Convolutional Neural Network (CNN) forensic discriminator. Leveraging the power of CNNs, which have been the cornerstone of image classification for many years, this discriminator aims to detect forged text and numeric images generated by GANs.


Document Forgey Detection with CNN

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout

input_shape = (128, 128, 3)  

model = Sequential([
    Conv2D(32, (3, 3), activation='relu', input_shape=input_shape),
    MaxPooling2D((2, 2)),
    Conv2D(64, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Conv2D(128, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Dense(256, activation='relu'),
    Dense(1, activation='sigmoid') 

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])


Incorporating generative AI into forensic science has the potential to revolutionize the field. From image reconstruction to fingerprint recognition and document forgery detection.

Key Takeaways

  • Generative AI, particularly GANs and Siamese networks, can enhance image reconstruction and fingerprint recognition in forensic science.
  • Code examples provided in this article offer a starting point for implementing generative AI solutions in forensic investigations.
  • Automating tasks using generative AI can improve efficiency and accuracy in forensic analysis.

Frequently Asked Questions

Q1. What is a Siamese network? How does it contribute to fingerprint recognition in forensic science?

A1. A Siamese network is a neural network architecture that compares and recognizes similarities between two input data points. Siamese networks are trained to learn representations of fingerprints. It can determine if two prints are from the same source based on the similarity of their embeddings.

Q2. Can GenAI recover information from heavily damaged or corrupted images in forensics?

A2. Yes, especially techniques like GANs, can significantly improve the quality of damaged or corrupted images. By training large datasets of clear and damaged images. GANs can reconstruct missing or distorted information, aiding forensic analysts in their investigations.

Q3. Are there any ethical concerns associated with using generative AI in forensic investigations?

A3. Yes, there are ethical concerns related to privacy, bias, and the potential for misuse when using AI in forensic science. Ensuring fairness and transparency in AI algorithms, maintaining data privacy, etc., are essential to address these concerns.

Q4. How can I obtain training data for implementing GenAI solutions in forensic science?

A4. Acquiring data for GenAI in forensic science may require collaboration with law enforcement agencies or access to publicly available datasets. Following legal and ethical guidelines is crucial when collecting and using such data.

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