Deepfake Forensic Analysis: Source Dataset Attribution and Legal Implications of Synthetic Media Manipulation

• URL: http://arxiv.org/abs/2505.11110v1
• Date: Fri, 16 May 2025 10:47:18 GMT
• Title: Deepfake Forensic Analysis: Source Dataset Attribution and Legal Implications of Synthetic Media Manipulation
• Authors: Massimiliano Cassia, Luca Guarnera, Mirko Casu, Ignazio Zangara, Sebastiano Battiato,
• Abstract summary: Synthetic media generated by Generative Adrial Networks (GANs) pose challenges in verifying authenticity and tracing dataset origins.<n>This paper introduces a novel forensic framework for identifying the training dataset (e.g., CelebA or FFHQ) of GAN-generated images through interpretable feature analysis.
• Score: 5.764826667785188
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Synthetic media generated by Generative Adversarial Networks (GANs) pose significant challenges in verifying authenticity and tracing dataset origins, raising critical concerns in copyright enforcement, privacy protection, and legal compliance. This paper introduces a novel forensic framework for identifying the training dataset (e.g., CelebA or FFHQ) of GAN-generated images through interpretable feature analysis. By integrating spectral transforms (Fourier/DCT), color distribution metrics, and local feature descriptors (SIFT), our pipeline extracts discriminative statistical signatures embedded in synthetic outputs. Supervised classifiers (Random Forest, SVM, XGBoost) achieve 98-99% accuracy in binary classification (real vs. synthetic) and multi-class dataset attribution across diverse GAN architectures (StyleGAN, AttGAN, GDWCT, StarGAN, and StyleGAN2). Experimental results highlight the dominance of frequency-domain features (DCT/FFT) in capturing dataset-specific artifacts, such as upsampling patterns and spectral irregularities, while color histograms reveal implicit regularization strategies in GAN training. We further examine legal and ethical implications, showing how dataset attribution can address copyright infringement, unauthorized use of personal data, and regulatory compliance under frameworks like GDPR and California's AB 602. Our framework advances accountability and governance in generative modeling, with applications in digital forensics, content moderation, and intellectual property litigation.

Related papers

• Attention-based Generative Latent Replay: A Continual Learning Approach for WSI Analysis [3.6630930118966814]
  Whole slide image (WSI) classification has emerged as a powerful tool in computational pathology, but remains constrained by domain shifts.<n>We propose an Attention-based Generative Latent Replay Continual Learning framework (AGLR-CL), in a multiple instance learning (MIL) setup for domain incremental WSI classification.
  arXiv  Detail & Related papers  (2025-05-13T12:55:46Z)
• Conditional Data Synthesis Augmentation [4.3108820946281945]
  Conditional Data Synthesis Augmentation (CoDSA) is a novel framework that synthesizes high-fidelity data for improving model performance across multimodal domains.<n>CoDSA fine-tunes pre-trained generative models to enhance the realism of synthetic data and increase sample density in sparse areas.<n>We introduce a theoretical framework that quantifies the statistical accuracy improvements enabled by CoDSA as a function of synthetic sample volume and targeted region allocation.
  arXiv  Detail & Related papers  (2025-04-10T03:38:11Z)
• Concept-Aware LoRA for Domain-Aligned Segmentation Dataset Generation [66.66243874361103]
  dataset generation faces two key challenges: 1) aligning generated samples with the target domain and 2) producing informative samples beyond the training data.<n>We propose Concept-Aware LoRA, a novel fine-tuning approach that selectively identifies and updates only the weights associated with necessary concepts for domain alignment.<n>We demonstrate its effectiveness in generating datasets for urban-scene segmentation, outperforming baseline and state-of-the-art methods in in-domain settings.
  arXiv  Detail & Related papers  (2025-03-28T06:23:29Z)
• Dataset Protection via Watermarked Canaries in Retrieval-Augmented LLMs [67.0310240737424]
  We introduce a novel approach to safeguard the ownership of text datasets and effectively detect unauthorized use by the RA-LLMs.<n>Our approach preserves the original data completely unchanged while protecting it by inserting specifically designed canary documents into the IP dataset.<n>During the detection process, unauthorized usage is identified by querying the canary documents and analyzing the responses of RA-LLMs.
  arXiv  Detail & Related papers  (2025-02-15T04:56:45Z)
• Detecting Dataset Abuse in Fine-Tuning Stable Diffusion Models for Text-to-Image Synthesis [3.8809673918404246]
  dataset watermarking framework designed to detect unauthorized usage and trace data leaks. We present a dataset watermarking framework designed to detect unauthorized usage and trace data leaks.
  arXiv  Detail & Related papers  (2024-09-27T16:34:48Z)
• GenDFIR: Advancing Cyber Incident Timeline Analysis Through Retrieval Augmented Generation and Large Language Models [0.08192907805418582]
  Cyber timeline analysis is crucial in Digital Forensics and Incident Response (DFIR)<n>Traditional methods rely on structured artefacts, such as logs and metadata, for evidence identification and feature extraction.<n>This paper introduces GenDFIR, a framework leveraging large language models (LLMs), specifically Llama 3.1 8B in zero shot mode, integrated with a Retrieval-Augmented Generation (RAG) agent.
  arXiv  Detail & Related papers  (2024-09-04T09:46:33Z)
• Thinking Racial Bias in Fair Forgery Detection: Models, Datasets and Evaluations [63.52709761339949]
  We first contribute a dedicated dataset called the Fair Forgery Detection (FairFD) dataset, where we prove the racial bias of public state-of-the-art (SOTA) methods.<n>We design novel metrics including Approach Averaged Metric and Utility Regularized Metric, which can avoid deceptive results.<n>We also present an effective and robust post-processing technique, Bias Pruning with Fair Activations (BPFA), which improves fairness without requiring retraining or weight updates.
  arXiv  Detail & Related papers  (2024-07-19T14:53:18Z)
• Unsupervised Contrastive Analysis for Salient Pattern Detection using Conditional Diffusion Models [13.970483987621135]
  Contrastive Analysis (CA) aims to identify patterns in images that allow distinguishing between a background (BG) dataset and a target (TG) dataset (i.e. unhealthy subjects) Recent works on this topic rely on variational autoencoders (VAE) or contrastive learning strategies to learn the patterns that separate TG samples from BG samples in a supervised manner. We employ a self-supervised contrastive encoder to learn a latent representation encoding only common patterns from input images, using samples exclusively from the BG dataset during training, and approximating the distribution of the target patterns by leveraging data augmentation techniques.
  arXiv  Detail & Related papers  (2024-06-02T15:19:07Z)
• Fake It Till Make It: Federated Learning with Consensus-Oriented Generation [52.82176415223988]
  We propose federated learning with consensus-oriented generation (FedCOG) FedCOG consists of two key components at the client side: complementary data generation and knowledge-distillation-based model training. Experiments on classical and real-world FL datasets show that FedCOG consistently outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-12-10T18:49:59Z)
• Domain Watermark: Effective and Harmless Dataset Copyright Protection is Closed at Hand [96.26251471253823]
  backdoor-based dataset ownership verification (DOV) is currently the only feasible approach to protect the copyright of open-source datasets. We make watermarked models (trained on the protected dataset) correctly classify some hard' samples that will be misclassified by the benign model.
  arXiv  Detail & Related papers  (2023-10-09T11:23:05Z)
• Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning [122.62311703151215]
  Divide and Contrast (DaC) aims to connect the good ends of both worlds while bypassing their limitations. DaC divides the target data into source-like and target-specific samples, where either group of samples is treated with tailored goals. We further align the source-like domain with the target-specific samples using a memory bank-based Maximum Mean Discrepancy (MMD) loss to reduce the distribution mismatch.
  arXiv  Detail & Related papers  (2022-11-12T09:21:49Z)
• FairGen: Fair Synthetic Data Generation [0.3149883354098941]
  We propose a pipeline to generate fairer synthetic data independent of the GAN architecture. We claim that while generating synthetic data most GANs amplify bias present in the training data but by removing these bias inducing samples, GANs essentially focuses more on real informative samples.
  arXiv  Detail & Related papers  (2022-10-24T08:13:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.