[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

This is a model made out of Neural Network specifically a Convolutional Neural Network model

Paddle implementation for "Cross-Lingual Word Embedding Refinement by ℓ1 Norm Optimisation" (NAACL 2021)

CAPRI: Context-Aware Interpretable Point-of-Interest Recommendation Framework

[CVPR 2021] Anycost GANs for Interactive Image Synthesis and Editing

A simple, fully convolutional model for real-time instance segmentation.

[ICCV 2021] HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration

CLADE - Efficient Semantic Image Synthesis via Class-Adaptive Normalization (TPAMI 2021)

ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

Jittor 64*64 implementation of StyleGAN

Roach: End-to-End Urban Driving by Imitating a Reinforcement Learning Coach

CenterPoint 3D Object Detection and Tracking using center points in the bird-eye view.

face_recognization (FaceNet) + TFHE (HNP) + hand_face_detection (Mediapipe)

Privacy as Code for DSAR Orchestration: Privacy Request automation to fulfill GDPR, CCPA, and LGPD data subject requests.

Deep motion transfer

Official code repository for the work: "The Implicit Values of A Good Hand Shake: Handheld Multi-Frame Neural Depth Refinement"

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)

Autonomous Ground Vehicle Navigation and Control Simulation Examples in Python

ppo_pytorch_cpp - an implementation of the proximal policy optimization algorithm for the C++ API of Pytorch

Extracting and filtering paraphrases by bridging natural language inference and paraphrasing

SafePicking: Learning Safe Object Extraction via Object-Level Mapping, ICRA 2022