[CVPR 2021] Pytorch implementation of Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs

Last update: Sep 05, 2022

Overview

Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs

In this work, we propose a framework HijackGAN, which enables non-linear latent space traversal and gain high-level controls, e.g., attributes, head poses, and landmarks, over unconditional image generation GANs in a fully black-box setting. It opens up the possibility of reusing GANs while raising concerns about unintended usage.

[Paper (CVPR 2021)][Project Page]

Prerequisites

Install required packages

pip install -r requirements.txt

Download pretrained GANs

Download the CelebAHQ pretrained weights of ProgressiveGAN [paper][code] and StyleGAN [paper][code], and then put those weights in ./models/pretrain. For example,

pretrain/
├── Pretrained_Models_Should_Be_Placed_Here
├── karras2018iclr-celebahq-1024x1024.pkl
├── karras2019stylegan-celebahq-1024x1024.pkl
├── pggan_celebahq_z.pt
├── stylegan_celebahq_z.pt
├── stylegan_headpose_z_dp.pt
└── stylegan_landmark_z.pt

Quick Start

Specify number of images to edit, a model to generate images, some parameters for editting.

LATENT_CODE_NUM=1
python edit.py \
    -m pggan_celebahq \
    -b boundaries/ \
    -n "$LATENT_CODE_NUM" \
    -o results/stylegan_celebahq_eyeglasses \
    --step_size 0.2 \
    --steps 40 \
    --attr_index 0 \
    --task attribute \
    --method ours

Usage

Important: For different given images (initial points), different step size and steps may be considered. In the following examples, we provide the parameters used in our paper. One could adjust them for better performance.

Specify Number of Samples

LATENT_CODE_NUM=1

Unconditional Modification

python edit.py \
    -m pggan_celebahq \
    -b boundaries/ \
    -n "$LATENT_CODE_NUM" \
    -o results/stylegan_celebahq_smile_editing \
    --step_size 0.2 \
    --steps 40 \
    --attr_index 0\
    --task attribute

Conditional Modification

python edit.py \
    -m pggan_celebahq \
    -b boundaries/ \
    -n "$LATENT_CODE_NUM" \
    -o results/stylegan_celebahq_smile_editing \
    --step_size 0.2 \
    --steps 40 \
    --attr_index 0\
    --condition\
    -i codes/pggan_cond/age.npy
    --task attribute

Head pose

Pitch

python edit.py \
    -m stylegan_celebahq \
    -b boundaries/ \
    -n "$LATENT_CODE_NUM" \
    -o results/ \
    --task head_pose \
    --method ours \
    --step_size 0.01 \
    --steps 2000 \
    --attr_index 1\
    --condition\
    --direction -1 \
    --demo

Yaw

python edit.py \
    -m stylegan_celebahq \
    -b boundaries/ \
    -n "$LATENT_CODE_NUM" \
    -o results/ \
    --task head_pose \
    --method ours \
    --step_size 0.1 \
    --steps 200 \
    --attr_index 0\
    --condition\
    --direction 1\
    --demo

Landmarks

Parameters for reference: (attr_index, step_size, steps) (4: 0.005 400) (5: 0.01 100), (6: 0.1 200), (8 0.1 200)

CUDA_VISIBLE_DEVICES=0 python edit.py \
    -m stylegan_celebahq \
    -b boundaries/ \
    -n "$LATENT_CODE_NUM" \
    -o results/ \
    --task landmark \
    --method ours \
    --step_size 0.1 \
    --steps 200 \
    --attr_index 6\
    --condition\
    --direction 1 \
    --demo

Generate Balanced Data

This a templeate showing how we generated balanced data for attribute manipulation (16 attributes in our internal experiments). You can modify it to fit your task better. Please first refer to here and replace YOUR_TASK_MODEL with your own classification model, and then run:

NUM=500000
CUDA_VISIBLE_DEVICES=0 python generate_balanced_data.py -m stylegan_celebahq \
    -o ./generated_data -K ./generated_data/indices.pkl -n "$NUM" -SI 0 --no_generated_imgs

[CVPR 2021] Pytorch implementation of Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs

Related tags

Overview

Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs

Prerequisites

Install required packages

Download pretrained GANs

Quick Start

Usage

Specify Number of Samples

Unconditional Modification

Conditional Modification

Head pose

Pitch

Yaw

Landmarks

Generate Balanced Data

Evaluations

TO-DO

Acknowledgment

Owner

Hui-Po Wang

EFENet: Reference-based Video Super-Resolution with Enhanced Flow Estimation

Generate vibrant and detailed images using only text.

Consistency Regularization for Adversarial Robustness

Prototypical python implementation of the trust-region algorithm presented in Sequential Linearization Method for Bound-Constrained Mathematical Programs with Complementarity Constraints by Larson, Leyffer, Kirches, and Manns.

PyGRANSO: A PyTorch-enabled port of GRANSO with auto-differentiation

Simple API for UCI Machine Learning Dataset Repository (search, download, analyze)

A Python wrapper for Google Tesseract

A collection of SOTA Image Classification Models in PyTorch

Adaout is a practical and flexible regularization method with high generalization and interpretability

Official PyTorch implementation of Joint Object Detection and Multi-Object Tracking with Graph Neural Networks

[ICCV 2021] Excavating the Potential Capacity of Self-Supervised Monocular Depth Estimation

Pointer-generator - Code for the ACL 2017 paper Get To The Point: Summarization with Pointer-Generator Networks

(AAAI2020)Grapy-ML: Graph Pyramid Mutual Learning for Cross-dataset Human Parsing

DLL: Direct Lidar Localization

Software associated to AAAI paper "Planning with Biological Neurons and Synapses"

A Light CNN for Deep Face Representation with Noisy Labels

Complex Answer Generation For Conversational Search Systems.

Code for our NeurIPS 2021 paper 'Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation'

Official PyTorch implementation of paper: Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation (ICCV 2021 Oral Presentation)

A simple python module to generate anchor (aka default/prior) boxes for object detection tasks.