Initial commit

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+.bin filter=lfs diff=lfs merge=lfs -text
+**/*.bin filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,4 @@
+**/Server
+**/Keys
+**/Client
+*-emb.txt

README.md

@@ -1,6 +1,6 @@
 ---
 title: Biometric Recognition FHE
-emoji: 👁
+emoji: 🧑 + 📸 + 🔐
 colorFrom: red
 colorTo: blue
 sdk: gradio

app.py

@@ -0,0 +1,306 @@
+
+import gradio as gr
+import numpy as np
+from PIL import Image
+from transformers import AutoImageProcessor, AutoModel
+import torch
+
+
+import timm
+import torch.nn.functional as F
+from torchvision import transforms
+import time
+
+
+import subprocess
+import os
+
+
+
+def resizeImage(image): 
+    resized = image.resize((112, 112))  
+    return resized
+
+
+
+def free_port(port):
+    try:
+        result = subprocess.check_output(f"lsof -t -i:{port}", shell=True).decode().strip()
+        if result:
+            for pid in result.split("\n"):
+                subprocess.call(["kill", "-9", pid])
+    except Exception as e:
+        print(f"Could not free port {port}: {e}")
+
+
+
+
+
+SECURITYLEVELS = ["128", "196", "256"]
+
+FRMODELS = ["gaunernst/vit_tiny_patch8_112.arcface_ms1mv3",
+            "gaunernst/vit_tiny_patch8_112.cosface_ms1mv3",
+            "gaunernst/vit_tiny_patch8_112.adaface_ms1mv3",
+            "gaunernst/vit_small_patch8_gap_112.cosface_ms1mv3",
+            "gaunernst/convnext_nano.cosface_ms1mv3",
+            "gaunernst/convnext_atto.cosface_ms1mv3"]
+
+
+
+
+def runBinFile(*args):
+    binary_path = args[0]
+    if not os.path.isfile(binary_path):
+        return "Error: Compiled binary not found."
+    try:
+        os.chmod(binary_path, 0o755)
+        start = time.time()
+        result = subprocess.run(
+            list(args),
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            text=True
+        )
+        end = time.time()
+        duration = (end - start) * 1000
+        if 'print' in args:
+            return result.stdout
+        elif 'styledPrint' in args:
+            return styled_output(result.stdout)
+        elif result.returncode == 0:
+            return True, f"<b>⏱️ Processing Time:</b> {duration:.0f} ms"
+        else:
+            return False
+    except Exception as e:
+        return f"Execution failed: {e}"
+
+
+example_images = ['./VGGFace2/n000001/0002_01.jpg',
+                  './VGGFace2/n000149/0002_01.jpg',
+                  './VGGFace2/n000082/0001_02.jpg',
+                  './VGGFace2/n000148/0014_01.jpg',
+                  './VGGFace2/n000129/0001_01.jpg',
+                  './VGGFace2/n000394/0007_01.jpg',
+                  ]
+
+example_images_auth = ['./VGGFace2/n000001/0013_01.jpg',
+                        './VGGFace2/n000149/0019_01.jpg',
+                        './VGGFace2/n000082/0003_03.jpg',
+                        './VGGFace2/n000148/0043_01.jpg',
+                        './VGGFace2/n000129/0006_01.jpg',
+                        './VGGFace2/n000394/0018_01.jpg',
+                        ]
+
+
+def display_image(image):
+    return image
+
+
+def load_rec_image():
+    return f'static/reconstructed.png'
+
+
+def extract_emb(image, modelName=FRMODELS[0], mode=None): 
+    transform = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.RandomHorizontalFlip(),
+        transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    ])
+    image = transform(image)
+    image = image.unsqueeze(0)
+
+    model = timm.create_model(f"hf_hub:{modelName}", pretrained=True).eval()
+    with torch.no_grad():
+        embs = model(image)
+        embs = F.normalize(embs, dim=1)
+        embs = embs.detach().numpy()
+        embs = embs.squeeze(0)
+        if mode != None:
+            np.savetxt(f'{mode}-emb.txt', embs.reshape(1, embs.shape[0]), fmt="%.6f", delimiter=',')
+    return embs
+
+def get_selected_image(evt: gr.SelectData):
+    return example_images[evt.index]
+
+def get_selected_image_auth(evt: gr.SelectData):
+    return example_images_auth[evt.index]
+
+
+def styled_output(result):
+    if result.strip().lower() == "match":
+        return "<span style='color: green; font-weight: bold;'>✔️ Match</span>"
+    elif result.strip().lower() == "no match":
+        return "<span style='color: red; font-weight: bold;'>❌ No Match</span>"
+    else:
+        return "<span style='color: red; font-weight: bold;'>Error</span>"
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Biometric Recognition (1:1 matching) Using Fully Homomorphic Encryption (FHE)") 
+    with gr.Row():
+        gr.Markdown("## Phase 1: Enrollment")
+    with gr.Row():
+        gr.Markdown("### Step 1: Upload or select a reference facial image for enrollment.")  
+    with gr.Row():
+        with gr.Column():
+            image_input_enroll = gr.Image(label="Upload a reference facial image.", type="pil", sources="upload")
+            image_input_enroll.change(fn=resizeImage, inputs=image_input_enroll, outputs=image_input_enroll)
+        with gr.Column():
+            example_gallery = gr.Gallery(value=example_images, columns=3)
+        with gr.Column():
+            image_output_enroll = gr.Image(label="Reference facial image", sources="upload")
+            image_input_enroll.change(fn=display_image, inputs=image_input_enroll, outputs=image_output_enroll)
+            
+    with gr.Row():
+        gr.Markdown("### Step 2: Generate reference embedding.") 
+    with gr.Row():
+        with gr.Column():            
+            modelName = gr.Dropdown(
+                choices=FRMODELS, 
+                label="Choose a face recognition model"
+            )
+        with gr.Column():
+            example_gallery.select(fn=get_selected_image, inputs=None, outputs=image_input_enroll)
+            key_button = gr.Button("Generate embedding")
+            enroll_emb_text = gr.JSON(label="Reference embedding")
+            mode = gr.State("enroll")
+            key_button.click(fn=extract_emb, inputs=[image_input_enroll, modelName, mode], outputs=enroll_emb_text)
+
+
+    with gr.Row():
+        gr.HTML("<h3>Facial embeddings are <span style='color:red; font-weight:bold'>INVERTIBLE</span> and lead to the <span style='color:red; font-weight:bold'>RECONSTRUCTION</span> of their raw facial images.</h3>")   
+    with gr.Row():
+        gr.Markdown("### Example:")   
+    with gr.Row():
+        original_image = gr.Image(value="static/original.jpg", label="Original", sources="upload")
+        key_button = gr.Button("Generate embedding")
+        output_text = gr.JSON(label="Target embedding")
+        key_button.click(fn=extract_emb, inputs=[original_image, modelName], outputs=output_text)
+        btn = gr.Button("Reconstruct facial image")
+        Reconstructed_image = gr.Image(label="Reconstructed")        
+        btn.click(fn=load_rec_image, outputs=Reconstructed_image)
+    with gr.Row():
+        gr.HTML("<h3>Facial embeddings protection is a <span style='color:red; font-weight:bold'>MUST!</span> At Suraksh.AI, we protect facial embeddings using FHE.</h3>")
+
+
+    
+    with gr.Row():
+        gr.Markdown("### Step 3: 🔐 Generate the FHE public and secret keys.")
+    with gr.Row():
+        with gr.Column():            
+            securityLevel = gr.Dropdown(
+                choices=SECURITYLEVELS, 
+                label="Choose a security level"
+            )
+        with gr.Column():
+            key_button = gr.Button("Generate the FHE public and secret keys")
+            key_status = gr.Checkbox(label="FHE Public and Secret keys generated.", value=False)  
+            time_output = gr.HTML()   
+            key_button.click(fn=runBinFile, inputs=[gr.State("./genKeys.bin"), securityLevel, gr.State("genkeys")], outputs=[key_status,time_output])
+    
+
+    with gr.Row():
+        gr.Markdown("### Step 4: 🔒 Encrypt reference embedding using FHE.")
+    with gr.Row():
+        with gr.Column():
+            key_button = gr.Button("Encrypt")
+            key_status = gr.Checkbox(label="Reference embedding encrypted.", value=False) 
+            time_output = gr.HTML()  
+            key_button.click(fn=runBinFile, inputs=[gr.State("./encReference.bin"), securityLevel, gr.State("encrypt")], outputs=[key_status,time_output])
+
+        with gr.Column():
+            key_button = gr.Button("Display")
+            output_text = gr.Text(label="Encrypted embedding", lines=3, interactive=False)
+            key_button.click(fn=runBinFile, inputs=[gr.State("./encReference.bin"), securityLevel, gr.State("print")], outputs=output_text)
+
+
+    with gr.Row():
+        gr.Markdown("## Phase 2: Authentication")    
+    with gr.Row():
+        gr.Markdown("### Step 1: Upload or select a probe facial image for authentication.")  
+    with gr.Row():
+        with gr.Column():
+            image_input_auth = gr.Image(label="Upload a facial image.", type="pil", sources="upload")
+            image_input_auth.change(fn=resizeImage, inputs=image_input_auth, outputs=image_input_auth)
+        with gr.Column():
+            example_gallery = gr.Gallery(value=example_images_auth, columns=3)
+        with gr.Column():
+            image_output_auth = gr.Image(label="Probe facial image", sources="upload")
+            image_input_auth.change(fn=display_image, inputs=image_input_auth, outputs=image_output_auth)
+    
+    with gr.Row():
+        gr.Markdown("### Step 2: Generate probe facial embedding.") 
+    with gr.Row():
+        with gr.Column():
+            example_gallery.select(fn=get_selected_image_auth, inputs=None, outputs=image_input_auth)
+            key_button = gr.Button("Generate embedding")
+            enroll_emb_text = gr.JSON(label="Probe embedding")
+            mode = gr.State("auth")
+            key_button.click(fn=extract_emb, inputs=[image_input_auth, modelName, mode], outputs=enroll_emb_text)
+    with gr.Row():
+        gr.Markdown("### Step 3: 🔀 Generate protected probe embedding.")
+    with gr.Row():
+        with gr.Column():
+            key_button = gr.Button("Protect")
+            key_status = gr.Checkbox(label="Probe embedding protected.", value=False) 
+            time_output = gr.HTML()
+            key_button.click(fn=runBinFile, inputs=[gr.State("./encProbe.bin"), securityLevel, gr.State("encrypt")], outputs=[key_status,time_output])
+        with gr.Column():
+            key_button = gr.Button("Display")
+            output_text = gr.Text(label="Protected embedding", lines=3, interactive=False)
+            key_button.click(fn=runBinFile, inputs=[gr.State("./encProbe.bin"), securityLevel, gr.State("print")], outputs=output_text)
+    
+    with gr.Row():
+        gr.Markdown("### Step 4: 🔒 Compute biometric recognition decision using the threshold under FHE.")
+    with gr.Row():
+        gr.Markdown("### Set the recognition threshold.")  
+    with gr.Row():
+        slider_threshold = gr.Slider(0, 512*5, step=1, value=133, label="Decision threshold", info="The higher the stricter.", interactive=True)
+        number_threshold = gr.Textbox(visible=False, value = '133')
+        slider_threshold.change(fn=lambda x: x, inputs=slider_threshold, outputs=number_threshold)
+    with gr.Row():
+        with gr.Column():
+            key_button = gr.Button("Biometric recognition under FHE")
+            key_status = gr.Checkbox(label="Recognition decision encrypted.", value=False) 
+            time_output = gr.HTML()
+            key_button.click(fn=runBinFile, inputs=[gr.State("./recDecision.bin"), securityLevel, gr.State("decision"), number_threshold], outputs=[key_status,time_output])
+        with gr.Column():
+            key_button = gr.Button("Display")
+            output_text = gr.Text(label="Encrypted decision", lines=3, interactive=False)
+            key_button.click(fn=runBinFile, inputs=[gr.State("./recDecision.bin"), securityLevel, gr.State("print")], outputs=output_text)
+
+
+    with gr.Row():
+        gr.Markdown("### Step 5: 🔑 Decrypt biometric recognition decision.")
+    with gr.Row():
+        with gr.Column(scale=1):
+            decision_button = gr.Button("Decrypt")
+            decision_status = gr.Checkbox(label="Recognition decision decrypted.", value=False) 
+            time_output = gr.HTML()
+            decision_button.click(fn=runBinFile, inputs=[gr.State("./decDecision.bin"), securityLevel, gr.State("decision")], outputs=[decision_status, time_output])
+        with gr.Column(scale=3): 
+            with gr.Row():
+                check_button = gr.Button("Check")
+            with gr.Row():    
+                with gr.Column(scale=1):                
+                    final_output = gr.HTML()
+                    check_button.click(fn=runBinFile, inputs=[gr.State("./decDecision.bin"), securityLevel, gr.State("styledPrint")], outputs=final_output)
+                with gr.Column(scale=1):
+                    image_output_enroll = gr.Image(label="Reference", sources="upload")
+                    image_input_enroll.change(fn=display_image, inputs=image_input_enroll, outputs=image_output_enroll)
+                with gr.Column(scale=1):
+                    image_output_auth = gr.Image(label="Probe", sources="upload")
+                    image_input_auth.change(fn=display_image, inputs=image_input_auth, outputs=image_output_auth)
+                
+
+
+
+    
+# preferred_port = 8080
+# free_port(preferred_port)
+
+# 
+# demo.launch(debug=True,server_port=preferred_port)
+
+
+demo.launch()

bin/decDecision.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f7675db4cd1c61743d73b78364ccefa313b408b09713e3b16e19f5a6eee087bb
+size 8045152

bin/encProbe.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3959987099fb521134b6c46c367b2b6f6106bf9bb45eda98108e26710376f078
+size 8030536

bin/encReference.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6705fbd4703ab2bc52d4af5719f5ece369de22d214dda966b124a85803c3a92d
+size 8069184

bin/genKeys.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:30c5e81854404a40c06ac9b727a17907536b1490defb055831921022b25e6e7c
+size 8079376

bin/recDecision.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:97d6a8be345463779f296ae73c70a082ea90e2c41add0f99f6875d75ff753c75
+size 8057936

lookupTables/Borders_nB_3_dimF_512.txt

@@ -0,0 +1 @@
+-0.050880,-0.029846,-0.014102,-0.000000,0.014102,0.029846,0.050880

lookupTables/MFIP_nB_3_dQ_0.001_dimF_512.txt

@@ -0,0 +1,8 @@
+5,3,2,1,-1,-2,-3,-5
+3,2,1,0,0,-1,-2,-3
+2,1,0,0,0,0,-1,-2
+1,0,0,0,0,0,0,-1
+-1,0,0,0,0,0,0,1
+-2,-1,0,0,0,0,1,2
+-3,-2,-1,0,0,1,2,3
+-5,-3,-2,-1,1,2,3,5

requirements.txt

@@ -0,0 +1,7 @@
+gradio
+transformers
+torch
+timm
+opencv-python
+pillow
+torchvision